Recommendation media is the new standard for content distribution. Here’s why friend graphs can‘t compete in an algorithmic world.
Meta, the company behind Facebook, recently announced a shift towards an algorithmic, recommendation-based model for content distribution on the Facebook newsfeed.
This is a significant change that marks the end of social media as we've known it.
There has been backlash, with influencers like Kylie Jenner expressing displeasure with Instagram's prioritization of recommended videos over content from friends.
Instagram's CEO, Adam Mosseri, acknowledged the need for change in a video discussing the platform's plans for the future.
This shift towards algorithmic feeds is in line with popular platforms like TikTok and YouTube, which prioritize carefully curated, algorithmic experiences.
Unlike social media, which relies on a user's social graph for content distribution, recommendation media focuses on delivering the best content to each individual.
This change has its advantages, such as reducing the spread of problematic content and creating a more efficient consumption experience.
But it also raises concerns about echo chambers and the control platforms have over content distribution.
Furthermore, it incentivizes creators to seek engagement on other platforms, driving growth for those platforms.
The rise of recommendation media also challenges the defensibility of social networks, as the underlying data that powers them, the social graph, has become commoditized.
Moving forward, it's possible that professional media platforms like Netflix may adopt recommendation media strategies, while also exploring AI-generated media to create more tailored content.
Overall, recommendation media is here to stay, and our consumption behaviors will continue to evolve as platforms gain more control over the content we see.
The future of social media remains uncertain, but it presents an opportunity for challengers to take a contrarian approach and reinvent the concept.