Three Clips Nobody Can Tell Apart
He was scrolling when a clip came up with a catchy title, an enthusiastic tone, and a fast cut at second three.
Then another clip, by someone else, with almost the same opening, the same tone, and a cut at the same second.
Then a third.
Three different people, and content with nothing to distinguish it.
And he knows why, because he does the same thing: open the tool inside the app, press "generate a title", then "generate an opening", then accept the first suggestion.
The tool gives the best average answer, so when everybody uses it, average becomes the only place there is.
The problem isn't the tool. The problem is that he delegated the part people follow him for.
The Rule Before the Tools
Tool names change every season, and the rule doesn't: AI is good at tasks with a known correct answer, and weak at tasks requiring personal judgement.
Write that dividing line down once, then apply it to any new tool that appears instead of rethinking from scratch with every update.
What You Can Safely Delegate
Subtitles and captions: transcribing speech is a mechanical task, and reviewing the output is faster than typing it yourself. Just watch for transcription errors in names and local terms, which are what automatic Arabic transcription gets wrong most.
Initial title suggestions: generating ten titles to pick one from is faster than waiting for inspiration. But the choice is yours, and the final wording usually needs your hand.
Repetitive editing tasks: audio cleanup, exposure correction, standard transitions. Nobody follows you for a transition between two shots.
Tag and keyword suggestions: a classification task, and the machine beats guessing at it.
What You Keep
The angle: why this subject, and from which side you approach it. This is a decision the tool has no information for: your experience, what you saw, who you met.
The judgement of what's worth publishing: the tool generates without limit, and doesn't know that a particular idea is worn out or doesn't sound like you.
The voice: the sentences that sound like you, the jokes your audience gets, the rhythm they've grown used to. Machine text is neutral by nature, and neutrality is the first thing to lose distinctiveness. Which is exactly what happened in the three clips at the top of this article.
The facts: any figure or claim coming out of a generation tool needs verification against a named source before it enters your script. That isn't pedantry, it's protection from a mistake published under your name.
The Decision Table
| Task | Delegate | Keep |
|---|---|---|
| Transcribing speech | ✓ | |
| Suggesting initial titles | ✓ | |
| Choosing the final title | ✓ | |
| Transitions and technical fixes | ✓ | |
| Deciding the subject's angle | ✓ | |
| Writing the hook | ✓ | |
| Suggesting tags | ✓ | |
| Fact-checking | ✓ |
A Practical Risk: Sameness
When thousands of creators use the same tool with the same settings, the outputs converge.
And platforms themselves have started distinguishing original content from recycled, as in the Facebook update covered in: Facebook's original content rules and what they mean for how you write. So relying entirely on automatic generation puts you in the category platforms are working to reduce the spread of.
A Suggested Way of Working
Write the angle and the hook yourself first, then use the tools for execution.
The reverse order, generating first and then editing, usually ends with text that doesn't feel like yours, and you spend longer fixing it than you would have spent writing it from the start.
And if you connect an AI assistant directly to your workspace, you can point it at a specific idea or card instead of working on pasted text, as in: practical examples of using Claude with Bardiyah via MCP.
A Note on How Fast Tools Change
The features mentioned in any article on this subject will change within months: names disappear and others arrive.
But the line between what's delegated and what's kept stays valid, because it's built on the nature of the task rather than the name of the tool.