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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.

Michael Mignano
6 min readAug 4, 2022


Last week, I published The End of Social Media and the Rise of Recommendation 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.



Michael Mignano

Partner, Lightspeed. Co-Founder, Anchor. Angel investor to 50+ startups. Former head of talk audio at Spotify.