Who Loses When the Algorithms Win?

While recommendation media promises users a better consumption experience in a post-social world, results may vary for some.

Last week, I published The End of Social Media, detailing how and why platforms were shifting away from social graphs and leaning into algorithmic, recommendation-based models of content distribution. If you haven’t read it, here’s the TLDR:

  • Distribution of content through friend graphs isn’t efficient for platforms. More importantly, it drives massive costs in the form of huge moderation teams, severe damage to platforms’ brands, and opportunities for challengers to find more efficient models.
  • Recommendation media, which distributes content via user-targeted algorithms is more efficient, more defensible, and less prone to abuse because platforms are in control of what gets seen and when, not creators.
  • In the future, platforms will seek even more control and efficiency in feeds, and will likely turn to forms of synthetic media to create the perfect content for each user at the right time.

The media platform landscape is vast, with myriad stakeholders contributing to the business of content being created, shared, and consumed on the internet. In a world where recommendations take over for friend graphs, it’s clear that the platforms themselves are clear winners that benefit from the paradigm shift. 

But who are the losers? Which stakeholders’ businesses will likely be disrupted as a result of this dramatic shift in content distribution? Let’s dive in…


Photos have long been one of the key forms of currency on social media. In the early days of social media, photos came in the form of family photos dumped into gigantic Facebook albums, giving people the ability to easily share pictures of family vacations or shots with their friends’ at last night’s party. Then, Instagram made everyone an artist through beautiful filters that could transform photos with the tap of a button. But it was Snapchat that turned photos into a true form of communication on social media through disappearing photos, and ultimately, stories (both boosted by an arsenal of fun tools to reduce the friction of sharing photos). As a result, sharing photos is now as casual and ubiquitous as sending text messages.

However, it’s no secret that videos have proven to be the far more valuable form of media when it comes to engagement, with nearly every platform doubling down on the format in recent years. Videos naturally convey far more context and information, therefore demanding more attention from consumers. If recommendation media is all about content distribution for the purpose of maximizing engagement, we should all expect to see a lot more video in our feeds. This will inevitably come at the expense of photos and the influencers who have built large followings (and careers) off of sharing photos to platforms like Instagram. As a result, I expect many photographers to explore new means of creative expression if they struggle to find distribution for their photographs.


But it’s not just the photo-sharing influencers who will be impacted; instead, it’s anyone who’s invested a large amount of time in building up their follower count. As I briefly mentioned in my previous piece, it’s no wonder why Kylie Jenner (one of the most-followed users of social media) opposes the shift to recommendation media: she will simply have much less programming power. Less programming power means less engagement from her content which means less demand from advertisers, brands, and sponsors for access to her followers.

But recommendation media won’t just affect Kylie Jenner and the world’s biggest influencers; it’ll reduce the overall value of an individual follower on all major platforms. Millions of people who have spent years investing in cultivating an audience for the purpose of distributing content will need to re-think (or abandon) their approaches to content creation in favor of new playbooks that prioritize creating hit content instead of personal brand loyalty. This will be especially challenging for creators given the lack of transparency around what specifically drives engagement via platforms’ algorithms. In social media, the playbook was simple: build a following, get distribution. In recommendation media, the playbook will instead be: create content and hope for the best. Through this lens, it’s easy to see how the business of being an influencer is about to change dramatically. 

Friends and families

Let us not forget the core reason why many of us started using social media in the first place: to connect with friends and family. Despite the downsides of friend-graph based content distribution (such as “guaranteed distribution” for problematic content, echo chambers, etc), social media has played an enormous role in our collective ability to stay connected as human beings over the past few decades. And the need for this type of remote connection has only increased over time as we’ve all moved more our lives online, especially during the COVID-19 pandemic.

The ability to easily share life updates with each other may not come as easy for much longer. In social media, we could share a photo to Instagram feeling confident many of our friends and family might see it in the coming days. But in recommendation media, that same photo would be at risk of being bumped out of the feed by a valuable video from a complete stranger. As a result, I expect people will become much more intentional about how and where they share personal content with friends, such as in private messaging apps such as iMessage, WhatsApp, or Messenger, but not on recommendation platforms. 


Broadly speaking, there are two key ingredients platforms need in order to have a successful recommendation platform: a huge, diverse catalog of content and best in class machine learning algorithms. The former is needed to ensure each unique consumer on the platform can be perfectly matched with content that best suits their unique interests, while the latter is needed to actually do the intelligent matching between constituents. Both necessitate massive platform scale and capital, which the major platforms already have. However, startups who are hoping to challenge the platforms will be at a greater disadvantage in a recommendation media world. Whereas many new social networks rely on friend graphs to distribute content, the platforms will be doing perfect matching of content and consumer with far greater efficiency through the strength of their best in class ML.

However, on the flipside, this new dynamic may also open a door for pure social media startups to find relevance. While it’s clear the major platforms believe a better business awaits them through algorithmic content distribution, that doesn’t necessarily mean a great business model can’t exist for a challenger through social distribution. Given the void in human connection that may increase as our newsfeeds contain less content from our friends and family, new startups will attempt to pick up the pieces. We’re already seeing this happen to some extent, with pure social apps like BeReal dominating the App Store charts. However, in order for these new platforms to maintain relevance, they’ll need to do something truly unique with their format so they can’t be easily replicated by the major platforms.

What else?

While this piece focuses on stakeholders who may feel direct impact from the shift to recommendation media, it’s likely there will be many more downstream implications that I’ve yet to consider. What do you think? Who else loses as a result of the shift to recommendation media? And more importantly, who are some of the less obvious winners (besides the platforms) of this platform shift? Follow me on Twitter and LinkedIn to let me know or to get more essays and analysis from me.

The End of Social Media and the Rise of Recommendation Media

Recommendation media is the new standard for content distribution. Here’s why friend graphs can’t compete in an algorithmic world.

Last week, Meta announced that the Facebook newsfeed would be shifting towards an algorithmic, recommendation-based model of content distribution. This announcement marked the most recent example of a major platform to formally make this shift, while other major platforms, including Meta’s Instagram, have been headed in this direction for a while. 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 and a half.

Want to watch or listen to this essay instead of reading it? I’ve also published it as a podcast on Spotify and YouTube.

There has been 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.

But first…what is was 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 the programming power. As a result, social media is effectively a competition based on popularity, not on 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. 

The cost of social media

But just as massively as social media platforms have grown and changed the way we all consume content, they have also wreaked havoc for 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 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, 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.

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 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 I call, recommendation media.

Enter 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 movies, that person will likely see a lot of movie 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, it’s no wonder why Kylie Jenner opposes this change; her more than 360 million followers are 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. 

Less trust and safety risk

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’s little creators can do or say to counteract this. Long gone are the days where a creator can complain about being deplatformed or shadowbanned because their followers aren’t seeing their content; in recommendation media, the algorithm is understood to be the final decision maker about what gains traction and what doesn’t. This gives platforms far more leverage to hide unwanted content and therefore reduce 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 to platforms like Instagram, Twitter, and Facebook. Creators are sharing content to 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 tens 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. 

What comes next?

With Facebook formally pivoting to recommendation media, it feels like a new era of the internet is upon us, and it’s hard to imagine what might come next. But just as we’ve seen in previous generations of the internet, platforms will always seek more efficiency as technology becomes more advanced. Here are a few predictions for where the world could go next.

Professional media will turn to recommendation media

Given the strength of recommendation media platforms like TikTok and YouTube, and the way traditional social media platforms are chasing them, it seems likely Professional Media platforms (such as Netflix) may try to follow suit (in fact, Netflix’s co-CEO, Reed Hastings, may have even foreshadowed this when he famously stated that his biggest competitors were TikTok and YouTube, both of which are open to any creator). However, in order to be able to match the exact right content with the exact right person, a platform needs an ocean of content, including extremely  niche content for every person on the planet. The only way to have that much content is to be an open creation platform where users of the platform are able to create on the platform. So, I expect Netflix and similar platforms to let anyone create, not just the professional studios.

Platforms will seek even more control

If recommendation media is about platforms having more control over the consumer experience, it’s not hard to imagine that platforms will ultimately seek even more efficiency by making their own content. We’ve seen professional media platforms do this on a smaller scale (e.g. Netflix making originals, etc). But to do this at the scale of an open creation platform, such as TikTok or Instagram, platforms won’t be able to rely on humans. They’ll instead need to rely on machines to create AI-generated media, or as my friend Matt Hartman calls it, synthetic media. Recently, OpenAI’s DALL-E 2 has shown the world just how powerful and human-like synthetic media can be, but it’s unlikely these capabilities will stop at still images. As the cost of AI content-creation solutions come down, I expect platforms to create more synthetic media over time to create even more perfect fit content for the right users at the right time.

RIP social media

Recommendation media is here. As a result, we’ll make fewer explicit choices (“these are my friends”) and more implicit choices (“this is where the algorithm recommends I should spend my attention”) about how, when, and why we consume content. In the near term, we may not notice much of a difference, but it’ll be fascinating to look back a few years from now and reflect on how our personal behaviors have changed.
What do you think? Is social media gone for good? Or does this create an opportunity for a challenger to take a contrarian approach and bring social media back from the dead? Get in touch with me on Twitter or LinkedIn to let me know.