RIP Social Media

A couple of weeks ago, Meta announced that the Facebook newsfeed would be shifting towards an algorithmic, recommendation-based model of content distribution. A sure approval that recommendation media is the new standard for content distribution. Here’s why friend graphs can't compete in an algorithmic world. The announcement marked the most recent example of a major platform formally making this shift, while other major platforms, including Meta’s Instagram, have been moving in that direction for a while. Forced by the Chinese-owned TikTok’s rate of growth and to a certain extent fuelled by an exodus of users from Instagram, one can understand why. Photo sharing in an app has no more appeal in comparison to sharing a video - the new adage "a video is worth a thousand pictures" seems to be in play. Switching tact seems to be the only recourse left for Instagram. Given Facebook’s relevance as the world’s largest social network, this change signals the end of social media as we’ve known it for the past decade.

There has been a backlash. Kylie Jenner, one of the world’s most influential users of social media, recently posted about her displeasure with Instagram prioritizing recommended videos over photos from friends. With more than 360 million followers on Instagram, Jenner’s influence can’t be ignored; the last time she complained about a change to a social network, Snap’s stock price fell by 7%. It’s therefore likely no coincidence that Instagram’s CEO, Adam Mosseri, posted a video discussing some of these recent changes and plans for the future. In it, Mosseri acknowledges that the world is changing and that Instagram must be willing to change along with it.

And yet, these shifts towards algorithmic feeds over friend feeds make sense. Platforms like the massively popular (and still growing) TikTok and YouTube put far less emphasis on friends and social graphs in favor of carefully curated, magical algorithmic experiences that match the perfect content for the right people at the exact right time. This is recommendation media, and it’s the new standard for content distribution on the internet.

What Is Social Media?

Social media is content (text, photos, videos, audio, etc) that is distributed primarily through networks of connected people. This means that some level of distribution is guaranteed for creators based on the creator’s social network of friends or followers. This dynamic puts an enormous amount of power in the hands of creators because it means they have built-in audiences to which they can broadcast content. In social media, creators have programming power. As a result, social media is effectively a competition based on popularity, not on the quality of content. It favors the creators with the biggest followings, the bigger the following, the bigger the potential for distribution and influence.

Through this distribution dynamic, social media platforms are able to scale extremely quickly. If a platform can build a social graph (which, in the earlier days of social media, was extremely challenging for platforms but has become increasingly less so over time), it can automatically have a built-in distribution system for serving engaging, highly relevant content to massive audiences.

But At What Cost?

But just as massively as social media platforms have grown and changed the way we all consume content, they have also wreaked havoc on platform companies, the internet, and more broadly, our world.
Built-in distribution for content to social networks has meant that people can share and spread problematic content just as easily as they spread good-natured content. If a bad actor wants to share problematic content on social media, the content can spread fast because of the guaranteed distribution to the person’s network of friends. Furthermore, because the content is primarily distributed to clusters of connected people, there is huge potential for echo chambers of groupthink on social media. Diversity of thought is, by design or accidental, at a disadvantage in social networks. When it rarely finds its way in through open comment sections, it’s often met with fierce opposition and resistance, creating polarizing arguments and conflicts, sometimes among some of the most powerful people in the world (Twitter vs Trump vs Nigeria's head of state, etc., etc).

Social media has also proven to simply not be that efficient in terms of matching high-quality content with a relevant audience. Just because people can easily distribute content to their friends or friends of friends doesn’t mean that that content will be interesting or relevant to the consumer. This is why, over time, social networks have started not only distributing content based on social graphs but also based on how much engagement content has received within those social graphs.

The above problems with social media in turn generate massive costs for platforms, in the form of gigantic moderation teams made of tens of thousands of people, severe damage to platforms’ brands, and openings for competition to find more efficient means for distributing content. And arguably, no platform has been better at exploiting the weaknesses of social media than TikTok, the platform which popularized algorithmic content distribution and gave birth to what some are now calling - recommendation media.

Recommendation Media

In recommendation media, content is not distributed to networks of connected people as the primary means of distribution. Instead, the main mechanism for the distribution of content is through opaque, platform-defined algorithms that favor maximum attention and engagement from consumers. The exact type of attention these recommendations seek is always defined by the platform and often tailored specifically to the user who is consuming content. For example, if the platform determines that someone loves football, that person will likely see a lot of football-related content because that’s what captures that person’s attention best. This means platforms can also decide what consumers won’t see, such as problematic or polarizing content.

It’s ultimately up to the platform to decide what type of content gets recommended, not the social graph of the person producing the content. In contrast to social media, recommendation media is not a competition based on popularity. Instead, it is a competition based on the absolute best content. Through this lens, one can see why so-called media influencers (Kylie Jenner et all) should be nervous. Their exaggerated matrix with over 360 million followers is simply worth less in a version of media dominated by algorithms and not followers.

A Better Consumption Experience

In recommendation media, the best content for each consumer wins. This means that consumers are always being recommended and actively served content best suited for them, creating a superior consumption experience at all times. Whereas in social media, people see content from their friends regardless of the quality of the content, in recommendation media, content distribution is optimized for engagement. This results in very little waste in a feed, and consumption patterns are highly efficient.

Platforms also get to decide what’s popular and when. In social media, creators maintain programming power over what gets seen and when. But in recommendation media, the platform is always in control. This is similar to how cable television networks and radio stations have operated for decades; they program all media based on editorial and business decisions. However, on a platform like YouTube or Instagram which contains billions of pieces of potentially programmable content, programming can occur across a multitude of dimensions, such as any user’s interests, demographic, or location.

Trust, Safety And Risk Averse

Since a platform is in control of what content gets served to who and when there’s no expectation that a creator’s social network is guaranteed to see their content. Therefore, platforms can also choose what not to program, and there are little creators can do or say to counteract this. Long gone are the days when a creator can complain about being de-platformed or shadow banned because their followers aren’t seeing their content; in recommendation media, the algorithm is understood to be the final arbiter about what gains traction and what doesn’t. This gives platforms far more leverage to hide unwanted content and therefore reduces the need for massive moderation teams. It’s not that these teams are no longer needed; they’re simply not needed to the same scale as in social media because distribution for certain types of content can be eliminated from a platform without changing the underlying structure of content distribution.

Massive Growth Potential For Platforms

Since there’s no guaranteed distribution for content through friend graphs in recommendation media, creators are incentivized to seek engagement elsewhere when they’re not getting it from the platform where they created content. Where do they turn for that engagement? Other platforms. This is why you often see so much TikTok content being shared on platforms like Instagram, Twitter, and Facebook. Creators are sharing content with networks where they already have audiences.

This has a second-order effect of driving massive growth to the original platform. As an example, each time content from TikTok is shared on Twitter, a user who wants to consume that content clicks through to consume it on TikTok. This not only drives engagement on TikTok, but when the content consumer isn’t already a user of TikTok, it drives new user acquisition as well. Now imagine this dynamic occurring ten of millions of times, each time someone shares content from a recommendation media platform, and it’s easy to see how this can result in sky-high growth potential.

More Defensible

In addition to the drawbacks of social media mentioned above, social networks are simply no longer defensible because the underlying data that powers them, the social graph, has become commoditized. By leveraging login APIs from Facebook or Twitter, or even connecting a product to a user’s smartphone address book, teams can now quickly spin up social networks through which they can distribute content based on social graphs.

But in recommendation media, the algorithms that power distribution reign supreme. These algorithms, which are powered by machine learning, are unique, valuable, and grow in power and accuracy as a platform scales. Therefore, only the biggest and most powerful platforms can afford investments in the best machine learning algorithms because they are such expensive and resource-intensive assets. In recommendation media, the platform with the best machine learning wins.

With Facebook formally pivoting to recommendation media, it feels like a new era of the web is upon us. It might be difficult to predict what will come next, this much though we know - platforms will always seek more efficiency as technology becomes more advanced.

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