All Podcast Roads Lead to Video

The format that got us all to listen is becoming visual, and podcasts will never be the same.

If you’re a regular consumer of podcasts, you may have noticed a change over the past few years: many of the world’s most popular shows (and maybe some of your favorites) have started including the ability to watch instead of simply listen. While video podcasts are not a new concept, they’re quickly becoming mainstream and will soon represent the majority of the world’s podcasts. As the co-founder of Anchor, the world’s biggest podcasting platform, I’ve paid close attention to this growing trend over the past few years.

Why is this happening?

The format of podcasts has long supported video. In fact, RSS, the standard by which most of the world’s podcasts are distributed, has always supported an option to indicate to platforms that an episode is a video file. However, lack of seamless support for shows that feature both video and audio has prevented most creators from utilizing this option, thus disincentivizing most major platforms from going all-in. Despite this limited demand up until recently, several major podcasters, including some of the most influential in the world (like Joe Rogan) have been publishing video podcasts for years. Now it seems nearly all podcasters are at least considering switching to video. But why?

COVID-19 and Social Distancing

Before COVID changed the way we all live and work in 2020, many of the world’s podcasts were recorded in real life. For shows with multiple hosts or guests, podcasts were often recorded in a studio or physical space, with creators huddled around a few microphones. Once COVID hit and we were all forced to social distance, people naturally started using more web-based capture tools to record their podcasts. Products like Zoom not only enabled us to hold virtual meetings, but they enabled podcasters to record podcasts remotely, too. Plus, Zoom and other dedicated podcast capture products like included additional features that standard podcast recording tools lacked: video capture. Virtually overnight, the people who were previously recording audio-only podcasts in a studio were getting video of themselves, their co-hosts, and their guests as a byproduct of social distancing.


Once podcasters had video to go along with their audio, it opened up a world of new possibilities for the distribution of their shows. No longer did it only make sense to publish their podcasts on Spotify and Apple Podcasts; now, they could also distribute to platforms like YouTube and have their content fit in right at home alongside an ocean of other videos, many of which didn’t look much different than their podcasts. Plus, they could take their video episodes and cut them into promotional clips that were easily shareable on social video platforms like Instagram and TikTok, giving them even more potential reach. For creators, this unlocked potential exposure to millions of new fans who weren’t using traditional podcast consumption products.


But it wasn’t all about distribution. Creators quickly learned, just as platforms did years ago, that video produced more engagement. Not only would people listen, but when their eyes were free, they would also watch, investing even more of their attention in their favorite shows, creating an even stronger relationship between fan and creator. And perhaps most importantly, it produced more revenue for creators and platforms given the relative value of video to audio for brands and marketers.

What happens as a result? 

The shift from audio to video for podcasts is only accelerating. Only a few weeks ago, YouTube announced more dedicated support for podcasts. And Spotify has recently expanded video podcasts to more creators around the world. But what happens next?

The opportunity for podcasts will get much, much bigger

Podcast revenues are expected to exceed $2B in 2022. YouTube, on the other hand, generated nearly $29B in video ad revenue in 2021 alone. In other words, the video market is vastly bigger than the podcast market. As more and more podcasters turn to video, more revenue will be unlocked for their shows. The opportunity for podcasters to generate meaningful revenue, and capture a slice of the overall video market, will grow significantly. This should be welcome news for any creator in the podcasting space. It’s long been discussed how challenging it is for podcasters to generate ad revenue because of the limited tools, data, advertisers, and infrastructure to support a significantly larger podcast ad market. But once podcasts are a part of the video ad market, all boats should rise. 

Products will evolve to meet demand

More and more tools will adapt to the ever changing world of podcasts. You’re already seeing this play out in real time. Before I left Spotify (where I led the Talk audio businesses) earlier this year, Anchor adapted its product to have better support for videos, and expanded the availability of these features several months later. Podcast editing software Descript seems to be making a big push into video. Podcast video recording platform Riverside recently raised $35M in venture capital. Following the consistent trend of all other forms of media on the internet, the friction to create video podcasts is likely to drop dramatically over the coming years, enabling many millions more people to participate in the medium. 

Content will evolve

Just as the products will evolve to meet the new demand for video, so too will creators’ content. Podcasters will look beyond the limitations of audio-only to create shows that may not even look or sound much like podcasts at all. After all… what’s the difference between a video podcast of people talking to each other and a traditional talk show? Not much. Major media platforms have begun shifting away from social media and more towards recommendation media (which I recently wrote about), which favors engagement over friend graphs. And engagement with video has proven to be much more valuable in this new distribution model. As a result, creators who want their content to be discovered will likely find themselves producing more video over time. All of this begs an important question… 

Will podcasts go away?

In a world where every podcast includes video, every platform supports video, and video-first shows are more engaging for users (and therefore more valuable for creators), it’s fair to wonder if people will stop making “podcasts” as we know them today. Today a podcast is an episode of audio content featuring people talking. Tomorrow, it seems as if a podcast will look a lot like the talk shows many of us grew up watching on television (and many watch on platforms like YouTube today). 

While this notion will likely irk some readers, consider the potential benefits to the medium. Podcasting has been greatly constrained as a business since its inception nearly 20 years ago. The vast majority of creators still don’t generate any money. Few podcasting businesses (hosting platforms, content studios, etc) have been able to generate meaningful revenue over a sustained period of time. And many millions of listeners have yet to be exposed to this incredibly rich and engaging format.

If podcasts do go away in favor of videos, it will likely be a result of the following:

  • Most consumers will prefer video over audio
  • Most creators will therefore prefer video over audio, because it drives more distribution and engagement
  • The podcast ecosystem as a whole will be generating far more revenue for creators and podcasting businesses

I’ve been a part of the podcast ecosystem for nearly a decade. For as long as I can remember, everyone has been waiting for podcasts to become a bigger business and more equitable for all stakeholders. Video may be the key. Podcasts may go the way of video, but it may actually be a very good thing for all involved.

What do you think?

Is video making podcasts obsolete? What do you think will happen as a result? Let me know your thoughts or feedback on Twitter or LinkedIn.

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.