The Creativity Supply Chain

Creativity has become the focal point of our modern Internet economy. What was once just a means of artistic expression now powers much of how we all interact, work, and play online.

People are inherently creative. As far back as the history books go, humans have always expressed themselves creatively, whether for communication, storytelling, or even art. As the tools that we’ve used to express ourselves have evolved and multiplied, so too have the manifestations of our creativity. 

Today, not only are the tools easy to use, they’re also increasingly powerful. Plus, we now have immediate access to a limitless ocean of others’ creative work through our powerful smartphones and a world washed in high speed internet. This combination of ease-of-use and instant, ubiquitous access to content has unlocked the inner creativity in all of us, unleashing a massive, humanity-sized addressable market of opportunity.

Creativity The Creator Economy

But wait, isn’t the Creator Economy dying? According to The Information, as of October 2022, venture funding for Creator Economy startups had fallen for the previous five quarters. The category, recently immortalized (and even more recently betrayed) by venture capitalists, startup founders, and tech journalists, was the promise that creators on platforms like YouTube, Instagram, and TikTok could now start making a living from their content, en masse. The Creator Economy promised to deliver not only tools to help creators make money, but ultimately, the potential for true economic independence. 

Pandemic about to take your job away? No problem; the Creator Economy is here to save you from unemployment and fund your daily Instagram habit. Right?

Not so much.

The Creator Economy is a sliver of a much bigger opportunity

As it turns out, the constrained opportunity of the Creator Economy hasn’t lived up to its sky-high expectations. As many creators quickly learned, finding 1,000 true fans is something very few people are actually able to do. Most creators can’t break through platforms’ algorithms. Most creators are not marketing experts. Most creators are not making content that is relevant to more than a handful of people. And no, I’m sorry to say, but most creators aren’t going to benefit from their content being “ownable” on the blockchain. This is not a criticism of most creators; it’s a simple acknowledgement that the market for creators’ work follows the same power law dynamic that most other markets face. 

All of this is completely OK; there is nothing at all wrong with creating for the sake of creativity.

In fact, there’s a strong case to be made that the aggregate value of all creator content in the 99% (referenced in the “everyone else” category in the image above) is actually equal to or greater than the value in the head (the “top 10,000 creators” referenced). The 99%, however, is much harder to build for, aggregate, and monetize, which is likely why it was so overlooked in the context of The Creator Economy.

And for the few creators who can find 1,000 fans, they can no longer reliably count on their economic support in a market downturn. After all, if you lose your job and your paycheck, which expense will you choose to eliminate first: groceries, or your monthly donation to your favorite YouTuber?

In hindsight, the reasons for the failed promise seem somewhat obvious: the Creator Economy was never really meant to be a solution for 99% of the hundreds of millions (or quite possibly billions) of creators that exist today; instead, it was designed to operationalize the financial infrastructure for the top 1% – the creators who have already broken through and found demand for their work. The Creator Economy was never about democratization; it was about elitism.

The Creativity Supply Chain

But creativity is universal. And the 99% matter. There’s arguably nothing more democratic than everyone on the planet being able to create and share their work with the world. 

What if the promise of the “Creator Economy” failed because we collectively (myself included) defined the opportunity for creativity as far too small? What if we were thinking about one small piece of the market for creativity? Surely there is much more to creativity than the tools that enable the top 1% of creators to make money. What if the opportunity for creativity is much bigger than what we’ve all been calling the Creator Economy? I believe it is. I call this opportunity, The Creativity Supply Chain.

You can think of it like this:

  1. Supply: The people that create for all purposes in today’s modern economy, and the creative media assets which they then distribute to their audiences.
  2. Incentives: Market mechanisms that financially and socially incentivize the creation, distribution, and consumption of creativity.
  3. Demand: The people that consume creativity and the channels by which creative media is distributed and consumed on the internet.
  4. Superpowers: Products, technologies, and services which democratize all forms of creative media, and give us unparalleled, immediate, and ubiquitous access to creativity. Today’s cutting edge technology both democratizes and turbo charges all of the above components, making them more accessible, efficient, and powerful. And the rate at which creative superpowers are improving is accelerating every day.

The Creativity Supply Chain is the global market for creativity in today’s modern economy. It is made up of the supply, incentives, and demand that drive how and why people create. And it rests on the technology-enabled superpowers which turbocharge all of the above, generating massive market value in the process.

Supply

I have always believed that people are inherently creative. I was raised by parents who encouraged me to pursue my many – and scattered – creative interests. I grew up playing the drums, learning to code in high school, studying Computer Science in college, and dabbling with music, photography, painting, and writing all along the way. As a career, my interest in creativity led me to build websites for artists at Atlantic Records and eventually build products which enabled hundreds of millions of people to take and edit photos at Aviary and Adobe. The belief that people are inherently creative was also one of the factors that led my former co-founder Nir Zicherman and me to build Anchor, a platform which democratizes podcast creation, and was acquired by Spotify in 2019.

But in 2022, I believe it should be obvious to nearly everyone that we are all creative. We write, take photos, edit videos, design presentations. We all create content using tools like our phone’s camera, Snapchat, Instagram, Facebook, TikTok, BeReal, Twitter, and YouTube. We make podcasts, webinars, short films, and crafts. We share the things we create with family, friends, our co-workers, and strangers all over the world. We spread knowledge with colleagues, partners, and prospective employers using tools like Google Docs, Coda, Photoshop, Final Cut Pro, Loom, and Descript, while sharing ideas, photos, memes from Giphy, and weekend plans with our friends and family using Pinterest, Google Photos, and WhatsApp. We’re all being creative all the time. We all create constantly, every single day.

Incentives

Our incentives to be creative are varied. We create to communicate. We create for money. We create to express ourselves artistically. We create because our work requires us to do so. We create for attention; we like the feeling we get when the view count on our video starts shooting up. Sometimes we create simply to make our friends laugh.

Technology has helped streamline these incentives. Notifications buzz our phones and inboxes to let us know when others interact with our content, giving us micro doses of joy and much-needed confidence. Our teammates quickly provide feedback on work in real time using a multitude of multi-player enabled creative tools. For those of us looking to monetize our work, we can easily sell our audience’s attention to the highest bidder on platforms like Instagram, YouTube, or TikTok, for we are today’s version of yesterday’s magazines, television stations, and billboards. With enough distribution, a few of us are even able to get our audience to pay us directly for our work through platforms like Patreon, Substack, OnlyFans, OpenSea, Fanfic, Pietra, and more.

Regardless of our own unique incentives, we all create because there is one universal incentive which all creators share in 2022: there is overwhelming demand for creativity. 

Demand

You are a perfect example of this demand. Think about it: how do you spend your time? You watch videos on YouTube, look at stories and photos and Reels on Instagram, get sucked into TikTok for hours at a time, interact with designs by your colleagues on Figma, laugh at memes your friends send you on Telegram, binge watch shows on Netflix, look at friends’ baby photos on Facebook, read thoughtful essays on Substack, listen to podcasts on Spotify, scroll through beautiful websites on Chrome, read books on Kindle, scan your local news, get dinner recipes on Pinterest… the list goes on and on and on. You are the ultimate destination for a long list of content that once began as a spark of creativity in the mind of another person somewhere else in the world. This essay, which you are reading right now, and the hand drawn graphics referenced throughout are all expressions of my creativity.

By this time next year, you will probably be consuming even more creativity as more products and services launch and your devices become more powerful. And while your demand for creativity continues to grow, so too does that demand for nearly everyone else on this planet; especially people who are just coming online for the first time in their lives, such as in emerging nations which are just gaining access to high speed internet.

For every region which is nearing saturation of creativity supply and demand, there is another one just getting started on the bottom of the S-curve. We are not at the end of something; we’re at the beginning of global creative ubiquity.

Superpowers

All of the above is happening in greater volume and velocity than ever before through what I like to think of as creative superpowers. Technology has made us all more creative through better tools. It has given us the ability to more easily achieve our creative goals (whether they be financial, social, or otherwise). And it has made it possible for us to find an audience for our work, no matter where in the world we are, physically. As we consume, we become more inspired to create ourselves, fueling a virtuous cycle of creativity that spins faster and faster as technology improves. 

Creative tools

The history of computing has proven time and time again that all forms of creativity eventually become democratized by technology. The costs of creating content are always being reduced by technology, and the abilities of people to create compelling content through technology are always increasing. 

This trend began long ago (Printing press? Invention of the pencil? Rock, blood, and berry juice used to scribble on walls of caves?), but perhaps became most apparent on the internet through text and publishing of the written word. It was previously challenging for non-journalists to publish written text. But eventually, Blogger, WordPress, and ultimately Medium made it vastly easier through easy-to-use tools and one-tap publishing capabilities. Platforms like Facebook and Twitter took it a step further by simplifying the format and enabling distribution of text to people within our social networks. Now nearly everyone writes and publishes some form of content.

The same happened for photos. It was previously extremely challenging for anyone other than professional photographers to take, edit, and share photographs at scale. Then, Instagram made it possible for anyone to take and share beautiful photos with the tap of a button. Now, everyone takes and shares photos. 

YouTube and TikTok did the same for videos. Anchor did the same for audio and podcasts. And the same can be said about a number of other categories. No-code website development products, platforms that enable the easy creation of video games, and even software that democratizes the creation of businesses/products are a few examples of the trend playing out in other categories. Many others will undoubtedly follow. 

Artificial intelligence

Technology-enabled creative tools have democratized creation to the point where any of us can create with the right tools, skills, and investment of time. But artificial intelligence (AI) is taking things many steps further. AI has recently reached a new tipping point, enabling any of us to achieve incredible feats of creativity by typing a few words into a text box. Cutting edge AI models such as Stable Diffusion, OpenAI’s DALL-E, and Midjourney make it possible to create visual images of limitless possibilities, making it possible for any of us to create things we previously could only imagine within seconds. 

And while today’s mainstream AI models are focused mostly on imaging, it’s only a matter of time before audio, music, video, text, and countless other creative mediums are soon disrupted by AI. Even writing code itself is in the process of being disrupted by AI.This past weekend, Nathan Baschez and the Every team introduced a product called Lex that helps writers by using AI to provide automatically-generated suggestions for what to say next. Within hours, Lex had thousands of people signed up to its waiting list. It’s not hard to imagine a world in which the cost of creativity plummets as a result of the rise of AI-enabled generative media. Ten years from now, will major movie studios be investing hundreds of millions in blockbuster films? Or will a state of the art AI model do all the work instead for orders of magnitude less?

Ubiquitous access to ultra fast internet

As of 2022, a 83% of the world’s population has access to a smartphone. That means that the vast majority of us on this planet are carrying around pocket-sized supercomputers virtually everywhere we go. And here in the US, many of us now have access to 5G internet at all times. With 5G, we’re able to download or stream massive amounts of creative media to our devices virtually instantaneously. Just think about everything you are now able to do from your mobile device. You’re able to watch full length movies with the tap of a button, access the world’s complete catalog of music, and even edit 4K video collaboratively, with your teammates, in the cloud. Ubiquitous, ultra fast internet makes the Creativity Supply Chain hum.

And there are major tailwinds behind this superpower. 5G market penetration is only at 13% globally and it is expected to reach 64% by 2030. As more people gain access to 5G in the coming years, it’s only going to get easier to instantly perform creative tasks and consume creative media. And 6G, which will be even more powerful than 5G and make creative mediums like AR and VR more accessible, is expected to begin rolling out in the year 2030.

Machine learning

One of the biggest breakthroughs for creativity over the past few decades has been the rise of media platforms such as Instagram, YouTube, Snapchat, Netflix, Spotify, TikTok, Twitter, Outschool, MasterClass, LinkedIn, and countless others across a variety of categories. While media platforms have traditionally relied on curated friend and interest graphs to match supply-side media with demand, platforms have recently learned that leveraging machine learning can drive far greater efficiency in distribution. As a result, media platforms across nearly every category – including social networks – are abandoning curation in exchange for recommendation media, which I recently wrote about extensively in a recent essay. Further advancements in machine learning and recommendation media will have massive implications on both the supply and demand sides of creativity. 

For creators, finding an audience for your creativity will happen automatically upon platform distribution. We’ll upload our content, and the platform’s ML algorithms will find the perfect audience at the perfect time for our work. And on the demand side, we will instantly and easily access the exact content we want to consume with far less friction. The supply and demand flywheel of creativity will spin far faster as a result of machine learning.

The Market for Creativity

Given how quickly all sides of creativity are becoming propelled by technology, it’s clear that each and every one of us will make up the total addressable market of the Creativity Supply Chain. But make no mistake; the Creativity Supply Chain isn’t some theoretical concept from the future; it’s happening right now. 

As of this writing, public companies with core businesses in the Creativity Supply Chain – such as Spotify, Shopify, Squarespace, Adobe, and Snap – represent more than $370B of market capitalization. And companies with big stakes in the Creativity Supply Chain – like Meta, Apple, Alphabet, and Netflix – represent more than $7T of market capitalization.

In private markets, look no further than the latest major technology acquisition – Adobe’s $20 billion acquisition of Figma, the largest ever acquisition of a venture-backed company – for proof that there is massive demand for companies contributing to the Creativity Supply Chain.

And Canva, a company that makes it easy for anyone to create beautiful images and graphics for both professional and personal use cases recently topped 100M monthly active users and is reported to be generating more than $1B in annual revenue.

The demand for Creativity Supply Chain businesses will only increase. As we all spend more of our time creating and consuming, and more of our personal and professional lives become transformed by technology, creativity will be even more entrenched in nearly everything we do on a daily basis.   

Creativity isn’t fleeting. Creativity is forever.

You can’t contain or limit the potential of creativity to specific incentives, such as the monetization of one’s top 1,000 fans. Yes, there will always be financial markets built around monetizing the work of top creators. But the reasons why people create are far more universal and fundamental to our common humanity, and these reasons result in a tremendous amount of aggregate value. And this is why the opportunity for creativity will continue to grow and flourish in lock step with technology. I like to imagine that today’s creative tools are the ones Steve Jobs was dreaming of when he first called computing a bicycle for the mind.

Everyone is creative. Technology increasingly fuels our collective ability to create, the incentives that motivate us to create, and our insatiable demand for the things others create. And through recent technological breakthroughs in artificial intelligence, machine learning, and high speed internet access, the market for creativity is destined to reach new heights in the coming years and decades.

What do you think? Is creativity becoming the central driving force behind our modern Internet economy? And what’s next for the technology that powers the Creativity Supply Chain? If you’re working in this space or have feedback on this essay, shoot me an email or reach out to me on Twitter or LinkedIn.

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 Riverside.fm 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.

Distribution

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.

Engagement

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 Power of SuperGoals

A framework and template for overcoming high-stakes challenges when everything is on the line

Most great teams would agree, there’s nothing more powerful for executing on big ambitions than a clearly defined and well-communicated goal. But sometimes the stakes are much higher than usual. In these instances, a goal requires even more focus, execution, and clarity. All of these can be achieved through a framework that I have come to call SuperGoals, which I’ve relied on heavily throughout my career.

Below, I’ll share my framework for SuperGoals, along with real life examples of how I’ve used them. But before I dive in, let me share some context to set the stage.

SuperGoals helped us revolutionize podcasting.

In 2015, while working at Adobe, my friend Nir Zicherman and I found we had a lot in common, and bonded over music, writing (you can read his work here), and most importantly… podcasts. After a decade of relative obscurity, podcasts were finally gaining mainstream appeal, attracting millions of people, including us. Nir and I both believed the medium would continue to gain momentum and were so enthusiastic that we decided to try to make our own podcasts. However, we were quickly demoralized by the high barrier of entry to the space, navigating complicated software, expensive hardware, and cumbersome file hosting and distribution systems.

Inspired by the personal challenges we faced with podcasting, we decided to start a company called Anchor to solve these problems, with a mission to democratize audio. Over the next several years, we would go on to create a category-defining product, build a world-class team, and ultimately sell Anchor to Spotify. After the acquisition, I operated at Spotify for several years, most recently leading the podcast, video, and live audio businesses, while helping grow Anchor into the world’s largest podcasting platform. While the story of Anchor had plenty of peaks and a very happy ending, it also had its fair share of valleys and near-death experiences. I believe that one of the keys to solving many of Anchor’s most difficult challenges was SuperGoals.

What exactly is a SuperGoal?

A SuperGoal is a high stakes, focusing goal for a team. It has a clear and urgent timeframe, an open-ended method of achievement, and a single measure of success that everyone can understand. While I’ve used a wide variety of goal types, such as OKRs, BHAGs, and stacked priorities, I have found SuperGoals to be my go-to framework in the most critical of moments.

Few goal-setting frameworks deliver the singularity of SuperGoals — you only get one, it’s not aspirational, and it must be accomplished.

When using SuperGoals, nothing else matters: they temporarily transcend all other goals by instilling a sense of urgency that reinforces clarity and inspires creativity.

When I use SuperGoals, they must meet three specific criteria:

  1. Clear and urgent timeframe: Many single-goal systems focus on “audaciousness” (e.g. “BHAGs”) in a way that acknowledges that a goal will probably never be hit. But my approach is the opposite — SuperGoals must be hit, and they require a concrete date by which it must be achieved. In this way, SuperGoals can drive urgency within a team. I highly recommend using SuperGoals in situations where there’s existential risk (i.e. if we don’t achieve X% growth by Y date, then we’ll run out of money and our startup is dead.)
  2. Open-ended method of achievement: While SuperGoals should be as precise as possible on the goal and the date, they should be the opposite in terms of the delivery model. They aim to encourage an “all hands on deck” approach, drawing ideas from all corners of an organization. In this way, SuperGoals can drive creativity amongst a team — allowing unexpected ideas to emerge from anywhere.
  3. Single measure of success that everyone can understand: “There can be only one.” If you follow the SuperGoal framework, you simply cannot have multiple goals at the same time. Also, everyone on your team must be able to understand it — it’s intended to drive clarity for the team.
SuperGoals (47).png

As I reflect back on my work on Anchor and Spotify, three stories of SuperGoals come to mind, each of which align with a specific phase of my overall journey. Each resulted in both amazing outcomes for our team and a distinct lesson about the value of SuperGoals: driving urgency, unleashing creativity, and providing clarity.

Lesson 1: SuperGoals drive urgency.

In the spring of 2017, about a year after Anchor launched to a wave of excitement (but before we found product-market fit), we found ourselves in a dangerous situation: unless we were able to raise our Series A round of funding during the following fall months, we would probably run out of money and the company would be over. Back then, there was a rigid seasonality to fundraising; the summer months were effectively dead, as was December and much of January. That gave us September, October, and November to get a deal done.

Sharp, aggressive goals are motivating.

There was just one big problem: since we hadn’t yet found product-market fit, our growth metrics were completely flat and unimpressive. We had exactly three months to turn the company around or it was over. Grow or die.

Nir and I locked ourselves in a conference room for a few days and raced to devise a plan. The main idea was to set an aggressive monthly active user number, roughly 4x our current amount, that we had to achieve by August. On the surface, this sounded good, but there were a few issues.

  • First, setting an MAU goal for a three month period meant that we were effectively giving ourselves only three data points to showcase dramatic growth. If we had even one bad month, the growth would look weak to investors.
  • Second, setting an absolute number as a goal meant it’d be hard to track our progress on a regular basis and gauge how we were progressing.

As a solution to the former, we adjusted to weekly active users (WAU), giving us roughly 9 data points to prove growth instead of 3. And for the latter, we drew inspiration from one of my favorite Paul Graham essays, Startup = Growth. In the piece, Graham notes that the most exceptional YC companies grow their revenue by 10% per week. While this growth rate seemed extreme, especially since we hadn’t yet found product market fit, we noticed that it would get us almost exactly to 4x our current scale when we worked it into our model. And with that, we set a company-wide SuperGoal of 10% week over week growth of WAU to ensure we didn’t run out of cash.

SuperGoal 1: Achieve 10% week-over-week growth of WAU for the next three months to ensure we don’t run out of cash.

High stakes spark ambition.

As a company, we met every day to track our progress and brainstorm new tactics. No idea was off the table, and everyone was expected to both contribute ideas and help execute on them, regardless of role or level. We also set a rule for ourselves: missing a week meant making up the missed growth in the following week, not resetting the baseline. This set the stakes even higher, because missing a week meant we’d pay for it later. During this period, some of our earliest growth drivers came from pure marketing tactics. For example, we got really good at promoting content from the platform and creating Twitter Moments that went viral, which helped grow our brand and drive user acquisition. However, we found that nothing was more impactful at moving the WAU growth needle than shipping and marketing new features.

Since SuperGoals have an open-ended delivery mechanism, the ideas for hitting them can come from anywhere. Given that, more than ever before, we were willing to solicit requests from our users, including ones that had previously violated some of our prior product principles. For example, one of our core beliefs was that our audio format was unique to Anchor and therefore could not exist on other platforms, such as Apple Podcasts and Spotify. However, we noticed an uptick of requests to enable Anchor-created content for off-platform distribution. This sparked a debate: should we go for it and change how Anchor operated?

In the early days, it wasn’t yet a podcasting platform; it was a social audio app that made it easy to create and share audio content. This part of our strategy meant that both creation and consumption could only take place within the Anchor app. If we chose to act on this request, we’d be breaking our strategy. With the stakes as high as they were, we swallowed our pride, pulled the request off the idea stack, and got to work.

Within a matter of weeks, the team built, shipped, and marketed an RSS delivery engine that enabled us to distribute audio to all major podcasting platforms with the tap of a button. And almost immediately, the feature changed the trajectory of Anchor. With this launch — which was only possible through lightning fast iteration and a sense of existential urgency that pushed us to question our original beliefs — we found product-market fit and hit our SuperGoal of 10% week-over-week WAU growth for three months straight. And we were able to raise a $10M Series A that September on the strength of our growth during that three month period. As I’ve reflected on this story in the years since, it’s become crystal clear to me how SuperGoals create urgency.

Lesson 2: SuperGoals unleash creativity and deliver unexpected results.

From that moment on, Anchor began growing like crazy, and quickly became one of the biggest hosting platforms. Best of all, as more people joined Anchor to create podcasts, we were helping to expand the size of the ecosystem, true to our mission. But a year later, a new challenge was upon us.

Through our research, we determined that only 1% of all podcasters in the world were able to monetize their podcast. This meant that the other 99% had no viable path to making any money and their podcasts would always remain a side hustle. For a company determined to empower creators of all sizes, we felt it was our responsibility to change this. We wanted to make podcasting more than just accessible; we wanted to make it equitable, too. To get to the next level of growth for Anchor, we had to break this pattern — we needed to make it possible for creators to make a real living off their podcasts.

Through Anchor’s large scale, we believed we could do something that had never been done before in podcasting: offer the world’s first scaled podcast ad marketplace. We envisioned brands could advertise across thousands of shows with a single click, just like they did with websites and search results through Google AdSense. We raced furiously over the course of many months to build and launch the product, with a hard deadline of launching what we would call “Anchor Sponsorships” by the end of calendar year 2018 so we could begin pursuing a Series B round of funding in early 2019.

Imagination is unlocked by constraints.

There was just one problem: while we raced to build the actual product, we were extremely demand-constrained, struggling to find brands willing to spend money on the platform before it launched. Given so few podcasters had ever been able to make money in podcasting before, we wanted to offer creators something groundbreaking — the ability to sign up for Anchor and start making money immediately. As a creator-first company determined to democratize podcasting, it would violate the entire value proposition if we didn’t have enough brand dollars to go around to creators the moment they signed up. After all, how could we offer an ad marketplace without having both supply and demand? It was a classic chicken-and-egg problem, but with a hard deadline of the product’s launch staring us in the face. It was time for a SuperGoal.

Once again, we set our bar high. After much deliberation, we decided creators wouldn’t be satisfied — and neither would we — unless every single creator who signed up got matched with at least one brand campaign at launch. So we made that our SuperGoal. By launch date of the product, we had to have enough demand on the platform to ensure every single podcaster could get sponsored.

SuperGoal 2: Ensure every single podcaster has a sponsor for their podcast by Anchor Sponsorships launch day.

The best ideas can come from anywhere.

We met daily to brainstorm and prioritize our goal’s work. Our first idea was to hire salespeople, which we began doing immediately. The few we hired — plus other team members (such as my co-founder and me) — got to work calling brands and pitching them on the concept. While we had success landing a few, we quickly realized that this slow, manual process would never help us reach our SuperGoal.

We moved on to agencies. Rather than spin up new brand relationships, we aimed to work with others who already had a stable of brands ready to spend money. While this sounded good in theory, in practice we found that the buying cycles were slow and seasonal. Plus, brands that worked with agencies insisted on buying proven formats only. With the launch only a few weeks away and the deadline for our SuperGoal fast approaching, we needed to try something crazy.

Then, one of Anchor’s engineers had a brilliantly creative idea: what if Anchor, the brand, became creators’ first sponsor? Initially, this idea sounded impossible. We were a tiny startup and didn’t have much of the $10M left in the bank. However, as we allowed ourselves to consider it and modeled the potential cost, we realized it had a shot at working. Since podcast ads traditionally paid on a CPM basis (cost per thousand impressions) and the world’s podcast catalog was mostly made up of smaller shows, funding most creators would be inexpensive. And for the shows that ultimately did break through and generate massive audiences, we felt confident we’d be able to help those creators sell their inventory to one of the agencies or brands we had partnered with and take a market-standard fee to cover our costs of the Anchor ads.

It worked. Sponsorships launched in November of 2018, with Anchor serving as the first sponsor for every creator who signed up for the service, followed by other sponsors like Squarespace, SeatGeek, Cash App, and others as creators’ shows grew. Within weeks, we had doubled the number of podcasters who had ever made money from their work. Not only that, but we quickly found that by having Anchor sponsor all creators’ shows, we actually grew our user base in a highly efficient way through word of mouth impact. Ensuring every creator had a sponsor bred profound creativity from all corners of our team and gave us a surprising new marketing channel. In this case, I found that SuperGoals unleash creativity and deliver unexpected results.

Lesson 3: SuperGoals provide clarity.

Shortly after this launch, based on the growing strength of our product and creator base, Spotify announced that it was acquiring Anchor in the winter of 2019. It was a dream come true for us to be continuing our work at Spotify, and the start of a new chapter for our team.

If you were following the podcast industry at the time, you may remember that the same day Spotify acquired Anchor, they also acquired another podcasting company, Gimlet Media. This was a meaningful moment for both the previously small podcast industry, as well as Spotify, the world’s leading music streaming service at the time. With this pair of acquisitions, Spotify was stating, for itself and the world, that it was no longer just about music: it was about the whole of audio.

While Gimlet and Anchor were both podcasting companies, the similarities ended there:

  • Gimlet, and other companies like Parcast and The Ringer (both of which Spotify would later acquire), were a bet that podcasting would be a hits-driven business, one in which the world’s most popular shows drove the vast majority of the value. It was easy to see why; companies like Netflix had built massive businesses worth hundreds of billions of dollars based on a similar premise, but for video instead of audio. By acquiring Gimlet, Spotify was placing one bet that audio will follow the same trend as subscription video platforms like Netflix.
  • Anchor, on the other hand, took a completely different strategic approach. We believed podcasting would eventually become a massive, creator-driven ecosystem, akin to open content platforms like YouTube or Instagram. We thought that by democratizing audio, we could both give everyone in the world a voice while also building an incredibly valuable audio business. By acquiring Anchor, Spotify was placing a simultaneous, yet complimentary bet that podcasting would be more like YouTube… but for audio. While each acquisition took a different approach to podcasts, Spotify had to find a way to measure all podcasts (and thus each acquisition) equally.

But how would they do it?

Nearly all mature internet businesses measure the value of their users through a metric called Lifetime Value (LTV). This helps companies determine which users are more valuable than others to help them figure out the most efficient marketing channels, the stickiest product features, and so on. But Spotify had an idea to take LTV even further. Using traditional LTV, as well as concepts like marginal churn contribution, Spotify was actually able to transform their LTV metric and apply it to not only users, but also podcasts. This meant that the value of every podcast on the platform — regardless of size — could be measured in the same way, apples to apples (you can see how Spotify talks about it here).

Through this powerful LTV metric, it was easy to see how massive, hit podcasts with millions of listeners (like ones Gimlet was producing) could be really valuable to Spotify. However, the idea that smaller podcasts could also drive meaningful LTV was still very much unproven.

Deep insights can drive breakthrough discoveries.

As we settled into Spotify, searched for our new identity, and wondered how we might contribute meaningfully to LTV, Anchor kept growing. More and more people were making podcasts. However, we felt we still needed to show the rest of the company, and the podcasting ecosystem more broadly, that smaller creators mattered. This felt existential; between speaking to many founders of other acquired companies and having gone through another acquisition myself (prior to Anchor, I was VP of Product for Aviary, which was acquired by Adobe), we knew that within big companies, acquired teams needed to prove their strategy’s value or risk being defunded.

Our Insights Lead had become intensely focused on Spotify’s LTV metric. After digging through Anchor data for months, he came to me with a discovery: there was in fact meaningful LTV being generated from smaller podcasts. He determined that this was because smaller podcasts, by definition, had niche audiences that were unlikely to overlap with other shows, resulting in more value on a per listener basis than larger shows. When looking at larger shows, they often drove much larger audiences. But those audiences tended to overlap quite a bit, so the marginal LTV of some of those shows was less than expected. But for the smaller shows, it was the opposite. This felt like a groundbreaking discovery. Smaller shows were punching way above their weight class.

In isolation, the value from a single smaller show wasn’t enough to deserve major attention within the company. However, based on this insight and how quickly this segment of the catalog was growing, we believed that the aggregate value of these shows could be extremely meaningful, potentially matching (or even exceeding) the value of the catalog’s head shows. Given the new discovery and the pressure we were already feeling, it was time for a SuperGoal.

Simplicity is powerful.

Once we saw this observation, we had a lot of debate on the relevant SuperGoal. Driving LTV can be a bit abstract for specific areas of a business, and as stated above, a SuperGoal needs to ensure an “all hands on deck” approach. LTV as a SuperGoal wasn’t going to work.

We then considered total catalog size, which we eventually determined could be misguided and incentivize us to prioritize features that only brought in new users. Instead, we needed a goal that pushed us to provide real value to creators – value that kept them coming back – not just enabled them to record one episode and quit.

Ultimately, we decided to make this goal simpler and relatable to everyone on the team by focusing on driving Monthly Active Creators (MAC). This covered new and existing creators to deliver long-term value for all creators — not just the new ones. I’ve chosen to omit the exact SuperGoal metric and date (as the team’s strategy is still in progress); however, I can assure you it was very ambitious and time-boxed.

SuperGoal 3: Prove Spotify LTV and the value of millions of podcasts by reaching X MAC by Y date.

What followed was one of the most collaborative, productive, and unifying periods of my Anchor/Spotify journey. We were able to ship cross-functional, groundbreaking features like Music+Talk, Podcast Subscriptions, and Video Podcasts. Not only did this SuperGoal roll directly into Spotify’s mission to enable a million creators to live off their art, it motivated our team to break free from our Anchor bubble to work with other teams at Spotify. In that period, we proved that millions of creators making podcasts on Anchor did drive LTV in a big way.

It also taught me another lesson: SuperGoals provide clarity. The broad impact we could have on the future of audio was now clear. And for Spotify, it clarified the value of all podcasts within the company’s broader strategy: Podcasting was no longer simply about the world’s most popular shows, it was about the many millions of creators, regardless of size, who chose to share their stories with the world.

Create your own SuperGoals to unlock new levels of success for your team.

SuperGoals unlock exceptional outcomes for teams when the stakes are highest. They supersede everything else by uniting colleagues with a shared sense of urgency while unleashing creativity and reinforcing clarity needed to bring them closer to the desired goal.  

If you and your team are ready to make your own, here are three templates to get you started:

  1. Choose your SuperGoal: Brainstorm potential SuperGoals as a team, select just one, and gauge everyone’s commitment to the SuperGoal.
  2. Ideas for your SuperGoal: Gather and nurture ideas from across the organization.
  3. Achieve your SuperGoal: Meet daily to check in on progress and discuss new ideas.

As you dig in, ask yourself: What is the single most important thing your team can accomplish right now?


I’d love to hear about your own experiences with SuperGoals! Reach out to me to share stories about how SuperGoals helped your team reach unprecedented success, or any other feedback whatsoever. You can find me on Twitter and LinkedIn.


Ready to set a SuperGoal?

Start by copying this doc, then head to Choose your SuperGoal.

Copy this doc


FAQ

1. How do you get people/team to drop other things and focus on this one thing — even when their heart and soul is on another project?

It’s important to emphasize that when using SuperGoals, all other goal-setting rituals must be temporarily suspended. While this is much easier to do on smaller teams with less overhead, process, and structure, it can also be accomplished on larger teams Perhaps most importantly, the larger the team, the more understandable the measure of success must be.

2. How do SuperGoals work in bigger companies?

SuperGoals don’t always need to be leveraged at the company level; they can also be used at the individual team level to help smaller groups focus on a hitting a goal they’re being pressed to hit in broader organizations.

Thanks to the following for their help and feedback: Nir Zicherman, Maya Prohovnik, Daniel Ek, Gustav Soderstrom, Matt Hartman, M.G. Siegler, Lenny Rachitsky, Lane Shackleton, Alex Taussig, Hunter Walk, Shishir Mehrotra, Erin Dame, Justin Hales, Harry Stebbings.

Image credit: NASA

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…

Photographers

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.

Influencers

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. 

Startups

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.

The Standards Innovation Paradox

Standards, like RSS for podcasts, have enabled emerging technologies to spread far and wide in the information age by making it easy for them to plug into existing ecosystems. But the blessing of standardization eventually comes at a cost, and innovation suffers as a result. As an example, this is why the podcast format has remained mostly stagnant over its 20 year history.

Technical standards are awesome. Standards help teams save time and money by giving them a common language for how their products can interact with other products, eliminating the need to build each component within a market or re-define how systems communicate with each other. For example, a team building a new email client doesn’t need to reinvent the format for how email is transmitted between sender and recipient; instead they can just adopt SMTP (Simple Mail Transfer Protocol, the standard that defines how email transmission works) and focus on crafting a great experience for their users. This means the wheel doesn’t get reinvented when someone wants to do something that’s been done before – they can just adopt the standard and accelerate their product development, reaching their audience – and oftentimes, product market fit – much faster than by building completely proprietary products.

Despite the benefit of standards-based products being able to reach an audience faster, the tradeoff is that a lower barrier to entry means more products get created in a category, causing market fragmentation and ultimately, a slow pace of innovation. I call this tradeoff the Standards Innovation Paradox, and I’ll explain it in more detail below.

But first…what exactly is a standard?

Simply put, a standard is a specification for how a technology (hardware or software) should talk to other technologies. Standards are generally developed by the community, but approved and maintained through consensus by committees which are typically open to anyone who wants to contribute. Some classic examples of standards in modern technology are HTTP (for web browsing), SMTP (for email transmission), RSS (for syndication of content, such as in blogs or podcasts), or SMS (for sending and receiving text messages).

Benefits of standardization

To understand the full scope of benefits that standards provide to product teams, it’s helpful to unpack an example, such as RSS (Really Simple Syndication) in podcasts. RSS has long been the backbone of podcasts, providing a powerful distribution mechanism that enables creators to publish their audio from a single endpoint and immediately syndicate their content to any consumption platform that wants to ingest it. RSS has enabled podcasts to flourish on the open internet over the past two decades by defining a language for how a vast network of podcasters and podcast listening apps communicate with each other. To publish audio via RSS, a creator (or podcasting platform, on the creator’s behalf) must publish the podcast in a specific format and include only the parameters defined within the standard, such as a URL pointing to the podcast’s cover art, a list of episodes, and so on.

I spent a lot of time working with RSS, having co-founded Anchor, a podcast creation platform that was acquired by Spotify in 2019. Anchor makes it easy for anyone, anywhere, to publish a podcast from iOS, Android, or their web browser without any prior experience or technical knowledge. One of the things that makes Anchor magical for creators is that it publishes podcasts via RSS to all podcast listening platforms with the tap of a button. This powerful distribution capability is one of the things that enabled Anchor to grow extremely quickly, and eventually become the world’s largest podcasting platform.

While RSS was a huge help for us building Anchor on the creation side of podcasting, RSS has also been instrumental to enabling the consumption side of podcasting. Virtually all of the world’s podcast listening apps that exist in the world of podcasts (such as Apple Podcasts, Spotify, Overcast, and many others) support the ingestion of RSS-powered podcasts. The benefit of doing so is huge: if a podcast listening app adopts this standard, it can automatically surface all of the world’s podcasts to its users, right away. Similar to the email example I used above, this means these listening apps can focus on a great user experience, but not have to worry about building out the content side of their business; the content already exists on the open internet, and can be easily pulled into the listening experience for users to enjoy. 

Trade-offs

Since adopting RSS saves podcast listening apps an enormous amount of time and money by not forcing them to reinvent the way content flows through the podcasting ecosystem, it means the barriers to finding an audience for these apps is lower. As a result, many of these apps exist, and thus a tremendous amount of market fragmentation has emerged within the podcasting ecosystem since its inception roughly 20 years ago. If you’ve ever searched the App Store or the Google Play store for a podcast app, you’ve likely come across a tidal wave of search results. In some ways, this fragmentation is great for users, because it means they have a ton of choice and flexibility in what product to use for their podcast listening. But at the same time, this fragmentation is bad for innovation, and makes it nearly impossible to innovate on experiences that are based on RSS, meaning the podcast listening experience has remained stale and largely unchanged for almost the entirety of podcasting. Why? As mentioned above, standards are consensus driven, meaning changes to the underlying language powering these podcast apps don’t come easily. To better understand this dynamic, consider the following analogy to planning a vacation.

The family vacation

Imagine you and your significant other are alone together on a vacation for two weeks in a country you’ve never visited before. Because it’s just the two of you, you can do anything you want on that trip without putting much thought into it. Want to cancel tonight’s dinner reservation and go to a concert instead? You can. Want to skip tomorrow’s museum visit and instead rent a car to go to a different city? You can. 

Now, imagine that same trip, but instead of it just being the two of you, your kids, your parents, your in-laws, three friends, your brother, his partner, and their four kids all tag along, too. It’s a completely different trip, right? In this version of the trip, everything has to be planned meticulously. And if you decide you want to make changes to the itinerary, you have to get everyone to agree, which is nearly impossible. What you end up with is a great time spent with family and friends you haven’t seen in a while, but a consensus-driven trip that is far less interesting and unique.

That’s what it’s like building products based on standards that have achieved scale and widespread adoption. Anytime a team wants to do something exciting and new that exceeds the limitations of the standard, they have to get every stakeholder (or at least enough to reach a critical mass of adoption) who has adopted that standard to also adopt the change, otherwise the change is useless. And if you plow ahead with the change anyway and break the standard, then you lose the benefits of the standard. This is hard enough with a bunch of friends and family on a vacation, but just imagine trying to do it with a variety of companies, big and small, all with different and potentially competing interests and priorities. This is the paradox of building with standards.

The Standards Innovation Paradox

The Standards Innovation Paradox is the trade-off teams face when building a new product based on standards; reaching product market fit can happen much faster because finding an audience for the product is easier, but the pace of innovation ultimately flatlines due to market inertia and consensus driven standards development. If and when a team decides to break the standard for the benefit of innovation without gaining buy-in from all other stakeholders, the benefits of the standard are lost.

Now, think about this in comparison to building in closed, proprietary systems which are not based on standards. When building everything from scratch, teams are free to implement and change technology however they see fit without having to worry about getting buy-in from misaligned stakeholders. The downside to this scenario is of course that development will be more expensive, and finding product market fit may be much more challenging. However, once a product finds product market fit, there’s no ceiling of the standard to prevent a team from accelerating their level of innovation.

The Standards Innovation Paradox

The Standards Innovation Paradox forces teams to make a choice when building new products that could be accelerated through standards: adopt a standard and get the immediate benefit of distribution/interoperability with a vast ecosystem of existing products (at the expense of long term innovation), or build everything from scratch to enable ultimate flexibility and innovation potential (at the expense of plugging into an existing audience)?

The Paradox in Podcasts

We faced this paradox with RSS when building Anchor in the early days, before we were acquired by Spotify. It was nearly impossible to make innovative changes to the podcasting format, because it was based on a virtually unchangeable RSS standard. 

For example, let’s say we wanted to enable a comments section for podcast episodes and have these comments be available within a show’s RSS feed. Unless we were able to get hundreds of podcast listening apps out there to adopt the change, the comments wouldn’t be supported on the listening side of podcasting. Without this support, there would be no incentive for creators to adopt and engage with comments either, and the feature would immediately fail.

As another example, let’s say we wanted to build a richer, more dynamic system for podcast analytics that enabled creators to better understand the performance of their shows, thus increasing their earnings potential through modern forms of internet advertising. Unless we were able to get hundreds of podcast listening apps out there to adopt the proposed change, getting the richer data from the listening apps back to the publishing platform wouldn’t be possible, and the innovation would fail.

This RSS-variety of the paradox has spawned a graveyard of podcast listening apps over the past two decades, many having tried to unsuccessfully build a differentiated podcast app on top of an entire ecosystem that’s based on a fully entrenched standard. 

The Paradox in Messaging

Here’s another example that highlights the limitations of building with standards: SMS, the text messaging standard. The invention of the SMS standard took place in the 1980s. After almost a decade, after getting all of the necessary stakeholders on board, it finally launched to the first mobile phone and cellular carrier in 1992, and eventually, reached scale in 1999 (remember: getting standards adopted requires an enormous amount of consensus). Once it did, anyone anywhere in the world could send a text message to any other person with a mobile phone that supported SMS, regardless of which provider or device anyone used. 

Then, someone had a brilliant idea to add a new feature to text messaging: pictures! How amazing would it be if you could send pictures via text message on your cell phone? But because SMS was an open standard, pictures couldn’t just be coded up into the latest software update. The standard itself had to change, and every device manufacturer and carrier had to agree to this change and adopt this change, via a new standard: MMS. And so it took almost another decade before MMS finally reached scale.

Now take iMessage, Apple’s proprietary messaging service, which is not at all a standard. iMessage is able to work because a critical mass of people quickly adopted an amazing – albeit proprietary – product: the iPhone. To use iMessage, you must own an Apple device, like an iPhone, which is certainly a drawback. And if you message someone else on an Apple device, you get the benefits of the service itself improving at an extremely rapid pace. By building in their own proprietary ecosystem, Apple has been able to innovate quickly on the messaging experience, and it now looks nothing like SMS ever could.

A brief history of messaging, with and without standards

Just think about how much iMessage has changed over the years. In the early days, it was indistinguishable from SMS. But now, it’s extremely rich with features like read receipts, photo galleries, face filters and Memojis, an App Store, voice memos, and the list goes on. And the same can be said about Snapchat, Messenger, WhatsApp, and many other proprietary messaging platforms. The only way these platforms were able to reach this level – and pace – of innovation was by building outside of the SMS standard (though, importantly, this came at the expense of being able to interact with other systems, thus limiting the potential audience).

The Paradox in Newsletters

Here’s another more recent example. You’ve likely heard of the amazing newsletter product, Substack. It’s a platform that enables creators to build, host, and scale their own newsletter businesses. The smart thing about Substack is that it uses an open standard – in this case, SMTP, the standard that powers email – to easily distribute newsletters to anyone who has an email inbox. 

In contrast to the podcast example above, where any platform that adopted RSS could instantly have the supply side of the chicken and egg problem solved, Substack did the opposite: it solved the demand side by ensuring all of its consumers already had a way to read newsletter content. This is a really smart strategy, and so as a platform, it has taken off quickly, attracting tons of high profile writers and plenty of paying subscribers. 

But despite the amazing ability to tap into SMTP for instant distribution to readers, there’s a tradeoff with this approach: email is static, and as long as email clients are powered by the standard of SMTP, it will remain static. This means Substack cannot use email to do anything dynamic, like personalize the discovery experience of the reader in real time in the email client. Or include a dynamic comments section that updates in real time. Or implement any other sort of feature that would enhance the creator or reader experience but would require some sort of dynamic interface inside of an email client. Like in the podcasting example, doing so would require getting most major email clients on the internet to adopt Substack’s innovations.

And so they did something recently that was very smart, but perhaps not surprising given the limitations of standards: they launched an app that enables them to build out their own rich experience for email newsletters. This makes a lot of sense, in my opinion. If Substack is able to scale its app successfully, it can rapidly innovate on the newsletter experience, and not be beholden to the standard of SMTP. But by doing so, they are sacrificing the benefits of the open standard which initially they used to kick start the demand side of their business.

It seems to me that Substack was faced with the Standards Innovation Paradox: keep building on top of SMTP to get the benefits of widespread email adoption? Or build a proprietary solution to accelerate the pace of innovation? With the release of its app, it’s clear to me that Substack has chosen to begin moving away from standards. 

Breaking the Curse

While the curse of the Standards Innovation Paradox can doom any fast moving company that wants to reinvent their category, it can be broken. In fact, there is a way for teams to have their cake and eat it, too, whereby they can both get the benefits of the standard, while also innovating past its limitations.

Leverage distribution from proprietary systems

After enough time, all of the products that adopt standards at scale will end up offering roughly the same experience. This is because there is a ceiling of what they can offer because of the entrenched nature of the standard. The more products that adopt the standard, the more market inertia, and the harder it is to change. This means competition is fierce, and it is unlikely that any one product will break out because of some differentiated experience. So how does one of these products break through and find a critical mass of adoption? To find distribution, these products need to piggy-back off of some other product that is not competing in a standards-driven market.

Think about Spotify’s podcast business as an example. A few years ago, the streaming audio giant evolved from being only a music service to being one for other categories of audio, such as podcasts. Given the content and experience differences between music and podcasts, many hoped the company would launch a dedicated podcast listening app to offer users a clean separation between the two content types. However, if they had done so, they’d have to contend with the aforementioned ocean of podcast listening apps which were all offering users roughly the same features that were limited by the standard. It would be just as challenging to breakthrough for a Spotify podcast app as it has been for every other podcast listening app. So instead, Spotify used their existing music user base inside of the existing Spotify app to distribute podcasts to hundreds of millions of users. By doing so, Spotify was able to break the curse of the paradox.

Deliver backwards compatibility

It’s important to remember that customers like using products based on standards because doing so offers them choice and data portability. If a standards-based product happens to break through market fragmentation, it’s important to maintain the benefits users got from the standard in the first place, otherwise you risk alienating your users and losing product market fit. The best way to do this is to ensure backwards compatibility with the standard. Take Apple’s iMessage as an example. If you’ve ever used iMessage, you’ve almost certainly messaged someone on an Android device. Notice how the bubble turns green? That’s iMessage falling back to the standard of SMS to interact with the recipient. This is the best of both worlds. For you and your friends on Apple devices, you can get all the benefits of an innovative, proprietary platform. But these benefits don’t come at the expense of the core messaging functionality which is based on the open standard, because you’re still able to message people on Android devices through SMS. 

To Standard or Not to Standard?

Despite the Standards Innovation Paradox, it’s impossible to ignore the massive benefits standardization has had on the success of technology over the past several decades. However, when building a new product that conforms to a standard, it’s always important to consider the trade-offs and weigh the future potential of being hindered by the paradox after a team finds product market fit.

Have you noticed other examples of the Standards Innovation Paradox out in the wild? If so, I’d love to hear about them! Just reach out to me on Twitter or LinkedIn.