Why platforms distribute less generic and repetitive content

Think about the leadership videos that crossed your feed this week. How many opened with a rhetorical question, listed three lessons and closed with a motivational line? And how many of them do you remember now? Platforms ask a similar question every day, and the rules they publish show how they answer it.

In this third article of the series, we bring together the official policies of Instagram, Facebook, YouTube and LinkedIn on repetitive, generic and mass-produced content. We also take a careful look at a study that points in the opposite direction and explain why it does not give a green light to the script with no idea of its own.

Recommendations: where a profile's reach grows

Every social network works with two audiences: the people who already follow the account and the vast majority of users who do not know the profile yet and will only find it if the platform decides to recommend a post. Recommendations are where you, as a leader or as a brand, gain new followers, customers and job candidates.

In April 2026, Instagram made clear how it treats accounts that mainly publish unoriginal content. They are dropped from recommendations, and people who already follow them keep seeing their posts. Eligibility returns when most of what was published in the last 30 days is original. The company reported that 75% of recommendations in the United States already come from original posts. In 2024, Instagram had already announced a new ranking signal to give more distribution to smaller creators with original content.

Meta's originality criterion deals mainly with content copied or recycled from third parties, and the second article in the series details those rules. The same 2026 page goes further and defines as original the work that reflects the "unique perspective" of the person posting. A script that could be read by any executive in any industry is far from that description.

YouTube and the inauthentic content policy

Among the video policies, YouTube's came closest to addressing generic scripts. In July 2025, the company renamed its "repetitious content" policy to "inauthentic content," with the stated aim of making clear that it includes repetitive or mass-produced material. The principle is that monetized content should not be "mass-produced, generic, repetitive or manipulative."

This policy comes with caveats, and you need to know them. It deals with monetization through the YouTube Partner Program and leaves video ranking aside. Rene Ritchie, YouTube's head of creator liaison, called the change a "minor update," according to eWeek. Social Samosa reported that the platform said the rule does not specifically target videos made with AI. Even so, the policy text describes with precision the kind of video a formula script produces: a template structure, a standardized narrative and little variation from one episode to the next.

Repetition within your own profile

One point of YouTube's policy deserves the attention of anyone who posts every week: minimal variation between videos. This risk shows up on the leader's own profile. When someone asks the chatbot every Monday for "an inspiring script about leadership for LinkedIn", the profile tends to pile up, in two months, eight videos with the same opening, the same cadence and the same kind of ending. Taken together, the eight videos form a predictable sequence for the follower and for the platform.

LinkedIn was already dealing with this issue before the AI wave. In 2024, Adam Walkiewicz, head of feed relevance, told Fast Company that the network has "robust defenses to proactively identify low-quality content" and exact or near-exact duplicates. At Meta, Facebook's 2025 rule provides for reduced distribution of duplicate videos. The three companies use different vocabulary to describe the same discomfort with content that repeats itself.

The Buffer study and the limits of its conclusion

A Buffer study points in the opposite direction, and we make a point of looking at it, because it is large and honest about its own limitations. In October 2024, the company analyzed 1.2 million posts and found a median engagement of 5.87% on posts made with the help of the tool's AI assistant, compared with 4.82% on posts without AI. On LinkedIn, the figures were 6.85% and 6.22%.

Buffer itself points out the bias. The most active users are the ones who use the assistant, and the gain would come from posting volume and consistency. The study compares posts made with and without the tool's assistant. It shows that AI can help someone post regularly, and none of its numbers deal with the generic script that repeats the formula of thousands of profiles.

In our view, using AI in the process is compatible with good performance. The problem described by platform policies is content with no variation, no perspective and a mass-produced look, whatever tool produced it.

What you gain from content only you could publish

The platforms' official pages offer the most useful clue. Facebook for Creators states that authentic stories "are more likely to perform well." Instagram links originality to the perspective of the person posting. YouTube accepts the use of AI when the result expresses the author's creative vision. All of these formulations point to the same place: the story, the opinion and the background of the person who appears in the video.

For you, this means that the most valuable asset of your social media profile is beyond the reach of any language model. It could be the customer who changed the way the company works, the costly mistake of 2019, the conversation with the longest-serving employee at the plant, the difficult decision made in a Friday meeting. A script built from this material is unlikely to look like any other, because no one else has access to it.

The fourth article in the series shows the ChatGPT phrases audiences already recognize in videos and captions, and the sixth details what LinkedIn now does with the posts it classifies as AI slop.