Fictional Reality: Why the Real Metaverse Doesn’t Require a Headset

AI is letting us live out alternative realities on the digital platforms where we already spend our time.

Much has been said of, written about, and invested in the “Metaverse.” For the uninitiated, the Metaverse is a conceptual version of the future in which we’re all interacting — for work, play, and everything in between — in an alternate version of reality that’s powered by VR, AR, BCI (brain computer interfaces) or a combination of all of the above. This promised future requires step changes to the behavior of how we all interact with both hardware and software on the internet. As a result, many billions of dollars have been invested in this future by countless companies, none more than Meta (fka Facebook), which went as far as rebranding themselves to signal their dedication to the cause.

But what if the internet, mixed with recent breakthroughs in artificial intelligence, is already enabling us to live in an alternate version of reality? Up until this point, we’ve been living our lives online in digital replicas of the real world, all with varying degrees of fidelity. Some are low fidelity, and mostly made up of text, photos, and asynchronous videos (e.g. social media platforms, email, messaging, etc), while others are much higher fidelity and more immersive, enabling us to get lost in experiences that more deeply connect with our senses (e.g. gaming, today’s VR/AR applications, etc). However, what all of these experiences have lacked thus far is a layer of high quality intelligence that’s been good enough to convince us that the people and experiences we’re interacting with on the other side of the screen are actually real.

All of this is now changing.

AI is making it possible for us to live out alternative versions of reality on the platforms where we’re already spending our time; not on futuristic devices and products that may not arrive for another decade. This is Fictional Reality, and it’s where much of our online behavior is headed.

But to understand what fictional reality is, we must first define reality itself.

What is reality?

Merriam-Webster’s dictionary defines reality as the quality or state of being real. I’d like to take that definition a step further and qualify it. I believe that reality is the overlap of three core elements of our lives:

  • Our real identity
  • Our real relationships
  • Our real, collective experiences
A venn diagram defining reality as the overlap of one’s Identity, Relationships, and Experiences.
Reality: the overlap of our identity, relationships, and experiences

We were all born with given names and appearances. Over time, we cultivate a cluster of personal and professional relationships. We collect unique experiences as we go about our lives, all of which shape who we are as people. For most of us, the vast majority of this reality has taken place offline, in the real world (aka IRL).

However, increasingly, over time, each of us are spending more and more of our realities online, in the digital world. This has meant mapping the three elements of reality — our identities, relationships, and experiences — to the digital world. For example:

  • Most of us use our real names on the various digital platforms we use on a regular basis. To represent ourselves visually, we upload real photos of our actual, IRL appearances as our avatars.
  • Our friends, acquaintances, and contacts littered across our social networks, email accounts, and address books often start by reflecting our IRL contacts.
  • The experiences we partake in online, such as socializing, professional work, education, and gaming, most often happen among groups of people who are being represented by their real identities. For example, I post to Twitter, Slack with my colleagues, learn new skills, and play games with my friends all under the guise of my real, IRL identity.

But when our real identities, relationships, and experiences start becoming fictionalized online, reality becomes fictional reality.

Enter Fictional Reality

Fictional Reality is the concept of people living, playing, or working online through fictionalized versions of their identity, relationships, and experiences. While this is not a brand new behavior, fictional reality is becoming more positive, commonplace, and accepted.

This new wave of fictional reality is different from internet anonymity, or even pseudo-anonymity. People have been pursuing versions of their lives with varying degrees of anonymity since the beginning of the internet. Oftentimes (though not always), the intent behind online anonymity has been for negative, cynical, or nefarious reasons, such as to hide the true intent or impression of one’s actions. “Finstagram” accounts, pseudonymous accounts on Twitter, and burner Reddit profiles are just a few of the many ways people have chosen to conceal their true identities and intentions on the internet.

But fictional reality is something much different; with fictional reality, people are crafting new versions of themselves online for positive reasons, such as to express creativity, gain confidence, or to share a part of their identity or interests that they aren’t able to showcase to the offline world. This may be because the fictionalized version of themselves isn’t socially accepted IRL, or it’s simply not technically possible. Fictional reality can even enable people to seek companionship not found offline or to express an opinion shunned by one’s IRL community.

A chart that shows the different ways whichReality and Fictional Reality are different from one another across Identity, Relationships, and Experiences.
Reality vs. Fictional Reality

And just like how reality is the overlap between identity, relationships, and experiences, the same is true in fictional reality.

Your fictional identity

Fictional reality starts with identity. Increasingly, more and more people are representing themselves through fictionalized names, backstories, and even avatars.

To some extent, this behavior goes back as far as AIM screen names; but look no further than Discord to witness the many new ways people are fictionalizing their identity through creative names/handles and backstories, signaling to the world that they are something other than their IRL counterparts. And this behavior is now extending to other platforms through visual identities and avatars, too.

As of this writing, it seems like half of the people I follow on Twitter have an avatar showing a graphical, highly exaggerated representation of themselves. Most of these are powered by an app called Lensa, which leverages generative AI, powered by Stable Diffusion (a text-to-image model created by, a Lightspeed portfolio company), to create these avatars based on 10–20 selfies you upload to the app. The app is immensely popular; it’s gone from relative obscurity to being the #1 overall app in the US App Store in a matter of days.

The explosion of popularity of Lensa — and therefore mainstream interest in artificial intelligence — is by itself interesting; what may be far more interesting is the sheer number of people who are excited to visually represent themselves as someone they are not IRL.

Not only are few challenging the usage of these fictional avatars, but instead, most are embracing it, rushing to the App Store to acquire their own. It seems like only a few short years ago, many of us were careful to not overly “Facetune” our selfies to reveal that they had been manipulated to conceal undesirable aspects of our true appearance. Yet all of a sudden, the opposite seems to be true: millions are excited to showcase the most exaggerated versions of ourselves online.

Side by side images of a selfie of Michael Mignano next to a fictionalized version of Michael Mignano generated by the app Lensa.
From left: nerdy IRL me, interplanetary warrior me (made with Lensa)

We may look back on this moment as the turning point in which many people on the internet became comfortable portraying themselves as fictionalized characters.

Your fictional relationships

But fictional reality is about more than just your identity; it’s also about the people — real or not — you choose to interact with.

If you imagine a world in which more and more of us are assuming fictional identities, then it’s only natural to assume that those we are interacting with online will be doing the same. Our friend graphs and inboxes, once dominated by real names and photos, may slowly shift to include more and more fictional characters. And in many cases, human beings won’t be controlling the identities; it’ll be artificial intelligence.

Look no further than ChatGPT, a conversational language model recently released by OpenAI that’s taken the internet by storm over the past week. If ChatGPT screenshots littering Twitter timelines and news articles are any indication, humanity has invested countless hours speaking to artificial intelligence — as if it were a human being — over the past week. In fact, on December 5th, only days after it had been launched, Sam Altman (OpenAI’s CEO) noted that 1 million people had already interacted with ChatGPT.

A screenshot of a human chatting with ChatGPT, a AI language model built by OpenAI. They are discussing free will and planning.
A conversation with OpenAI’s ChatGPT

But why limit our communication with artificial intelligence to a single text box? That’s where Circle Labs comes in.

Circle Labs (disclosure: a Lightspeed portfolio company) is proof that people are becoming increasingly willing (and excited!) to talk to fictional friends where they normally talk to real friends. Through the company’s creator platform — which enables anyone to generate their own AI-powered friends (aka shapes) and then deploy them to platforms like Discord and Twitter — people have exchanged millions of messages with Circle Lab’s artificial intelligence.

A Discord conversation between a human being and an AI-powered NPC.
A human chatting with a Circle Labs-powered “shape” on Discord

Your fictional experiences

And the ways people are interacting with these friends are fascinating; many are now deploying and including shapes as part of their IRL friend groups in their favorite online communities. For example: whereas it may have previously been five human friends chatting all day on Discord, now it’s those same five friends plus a few shapes to add a new dimension of companionship, creativity, entertainment, and conversation.

If fictional reality does end up looking like the version of Metaverse that’s been so heavily portrayed in demo videos, essays, news articles, and Meta (the company) presentations, it’ll be through experiences. Virtual events, concerts, online meetings, and social gatherings will serve as the experience layer that brings us together and shapes our fictional realities. The closer the experiences get to our senses (such as through VR headsets), the more lifelike these fictional realities will become. But the most obvious application of fictional experiences is already one of the most popular forms of media today: gaming.

So many of us already spend countless hours daily as fictionalized characters in games like Fortnite or on platforms like Roblox. This is not a new phenomenon. More than a decade ago, games like World of Warcraft (and EverQuest before that) served as havens for millions to escape their realities and get intentionally lost among clans of new online friends. They’d go on quests, overcome group challenges, and level up their characters in beautiful, vast, and fictional worlds.

A screenshot from World of Warcraft
A screenshot of World of Warcraft. Courtesy of dailyinvention under CC:

But lower fidelity — and lower friction — ways of sharing experiences among fictional identities are emerging. Mascot, a new social platform, enables anyone to create collaborative stories and fanfiction for their characters. Once users create and fully customize their characters (across a variety of attributes, including gender, pronouns, likes, dislikes, special powers, and backstories), they’re matched with other human-powered (yet fictional) characters with which they can create new stories to share with the world.

Many of the above games have their roots in paper-and-die games like Dungeons and Dragons and immersive fantasy novels that have been written for centuries. Imagining yourself navigating an alternate universe as an alternate version of yourself is clearly a fundamental impulse of human creativity. But now, AI is making it possible to go beyond imagining.

This is just the beginning. As more of us take on fictional identities and build networks of fictional relationships, more and more experiences will be purpose-built for this new version of reality.

What’s next?

Given the speed at which artificial intelligence is reshaping how we all interact online, it’s impossible to know what comes next. What’s clear, however, is that we need not wait for better, cheaper, faster — or more comfortable! — hardware (read: headsets) to give us permission to live in fictional realities. The digital platforms on which we already spend most of our time, money, and attention — plus AI — are already doing that for us.

Today’s fictional realities are well on their way to enabling many of us to live out more creative, expressive, and for some — human — versions of our lives. And for some, fiction may end up being far more interesting than reality. After all, if you spend more time on the internet than you do IRL, who’s to say which version of your identity is real and which is fictionalized?

What do you think? Are fictional realities already here? Or are we experiencing a temporary window of escapism along our path to a true, hardware-enabled Metaverse? I’d love to hear your feedback on Twitter or Linkedin.

Thanks to Paul Smalera, Meredith Kendall Maines, Moritz Baier-Lentz, Alex Taussig, and Faraz Fatemi for feedback and editing. Special thanks to Anushk Mittal, Noorie Dhingra, Artificial Intelligence, and tweets by Nikita Bier and Suhail Doshi for extra inspiration.

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.