How to Build a Better Social Network

The problem with most writing about technology is that it points out problems without proposing solutions. One of the easiest things to bellyache about is how social media works: it prioritizes shallow engagement bait by creators who already have large followings. In social media, the rich get richer, and the audience gets more stupid. 

I recently came across an article about the problem of social media that points out this problem and attempts to solve it. It’s by Nir Zicherman, the co-founder of podcasting company Anchor and the former VP and Global Head of Audiobooks at Spotify. He proposes a solution: create a market economy where users spend points in order to govern which posts get seen, and rapidly redistribute those points such that no one can accumulate an ongoing advantage. In this world, he hopes, the best content gets promoted—rather than just content from large accounts or shallow engagement bait.

Read the article and then see my commentary at the end. —Dan

I believe it’s possible to build an ideal social network that has all the benefits of social media while addressing the fundamental issues with today’s platforms. 

Here’s how it works: every new user is given 1,000 points when they join. When they see content they like, they allocate some or all of their points to the post’s creator. The more points a creator has at any given time, the more distribution their content gets. 

But the points decay over time in a predictable and transparent way. Gradually, points get reallocated across the user base. The more points you have, the faster you lose them. If you have less than 1,000, you’re incrementally brought back up. The network perpetually tries to return itself to equilibrium, and it’s the innovation of the community and proliferation of high-quality content that prevents that from happening. 

For the purposes of this article, I’ll refer to the network as DK (a phonic play on “decay”). And for the record, as the former vice president of audiobooks at Spotify and cofounder of Anchor, I’m not interested in building DK myself, but if you are, reach out and let’s chat. 

The social networks of today

We often hear about what’s wrong with today’s social networks: they’re echo chambers, the comments are toxic, clickbait proliferates, and the algorithms support extremism. Social media skews elections, spreads false information, and leads to mental health problems. The list goes on. 

And yet, the core concept—technology that brings us together by enabling seamless content distribution across the world—continues to attract people. 

Why, then, do our existing social media networks have so many flaws? Because social networks are economies. Self-serving agents interact in a public market, exchanging ideas and content instead of goods and services. They offer wealth in the form of distribution. They have winners and losers. 

But economies work when they are governed by supply and demand. Whatever is wanted spreads; whatever is low quality is weeded out. The invisible hand of the market functions when there are efficient correction mechanisms that reallocate money as needed. 

The social networks of today don’t have those correction mechanisms. There is no “free market” social network that exists at scale. Instead, we have one of two models. Most social networks lean heavily in one direction, but are in fact hybrids of the two.

The follower model

The traditional follower model—popularized by Facebook, Instagram, and Twitter/X—allows people to grow their distribution channels and retain them for future distribution. We’re all familiar with these. They come with five problems:

• Follower-based distribution: It is easier for people with large followings to distribute new content.
• Accumulated advantage: It is easier for people with large followings to get bigger. For example, an account can more easily go from 20,000 to 50,000 than from 10,000 to 30,000.
• Diminishing returns: As the network grows, feeds become crowded by incumbents, making it harder for new creators to succeed. 
• Echo chambers: Social connections are disproportionately made between people who think, look, and sound alike.
• Decreasing quality: Followers are easier to retain than grow. As a creator with a large following grows bigger, they have little incentive to invest in content quality. New posts do not need to work as hard as early ones to derive the same benefit for the creator. 

Over time, these social networks feel stale, tend toward extremism, and discourage content innovation. This is akin to an oligarchy. Some creators come in early, win big, and hoard their channels of distribution. 

Real economies have correction mechanisms. Two examples are depreciation and inflation, economic phenomena that incentivize innovation and capital investments. If a company does not continually strive for improvement, growth, and efficiency, its assets would depreciate and its prices would inflate. It would be driven out of business in the long term. 

An ideal social network will need to have similar counter-forces, so that content creators are incentivized to regularly improve and meet the needs of their audience.

The algorithmic model

The other type of social network, popularized by TikTok, follows the algorithmic model. Content spreads based on user signals around engagement. This algorithm is the primary decider of the type of content that succeeds.

Algorithmic models fail to be efficient markets for two reasons. First, they prioritize engagement over quality, which creates a self-fulfilling flywheel. Creators are incentivized to create content the algorithm prefers, which only strengthens the algorithm with more engagement-maximizing content. 

The second reason these models fail to be efficient is because the black box distribution algorithm is akin to an economic command economy, where the government controls market dynamics. There is little transparency and community control over the dominant algorithm, a structure that—according to most economists, at least—always results in long-term inefficiency. 

Both network models have structures that create problematic incentives for users. And neither model has sufficient correction mechanisms to ensure that the harmful aspects of social media don’t propagate. Which leads me to… 

Introducing a new social network: DK

DK is my concept of the “ideal” social network. Users will flock to this platform because of high-quality content. DK can be iterated on in a number of ways. I’ll keep it simple for the purposes of this article. 

A bad mockup of DK. Notice the feed is sorted by each creator’s current points.

Points: When a user joins a platform, they are given 1,000 points. Points are a store of distribution value. The more points you have, the higher you rank in the feed. 

The feed: The feed is global. It shows a stack ranked list of posts based on how many points each creator has. Creators who have created high-quality content are given points by others, ranking them higher in the feed and making their new content more likely to be seen. This is similar to the traditional follower model, except that there is an equalizing force, described below. 

Posts: Users can post content as often as they’d like. But only their most recent post shows up in the feed at any given time. 

Equalizing force: Every 10 minutes, the system slightly reallocates points. Think of it as a tax on users with high points that get reallocated to users with fewer points. The algorithm’s goal is to eventually return users to their starting condition—an equilibrium state of 1,000 points each. This is similar to the “correction mechanism” that markets have.

Here’s what the math of the equalizing force looks like: 

Every 10 minutes, an adjustment happens to all users’ points. This adjustment depends on the number of points a user has. This formula brings a user’s points back to equilibrium (1,000) at a rate of half every six hours. Think of it as a six-hour half-life. If the user is below 1,000, the process gives them some points, and it takes those from the users who are above 1,000. 

(This function is asymptotic, meaning it approaches the equilibrium of 1,000 but never quite gets there. For simplicity, let’s assume that when a user is within 10 points of 1,000, they just return to 1,000.) 

For example, a user starts with 1,000 points, posts decent content, and is given points by others. They now have 1,200 points. 

• In 10 minutes, they’ll have 1,196 points (four points taken away and given to other users on the platform).
• After 1 hour, they’ll have 1,178.
• After 6 hours, they’ll have 1,100 (half of the difference from 1,000 they started at).
• After 24 hours, they’ll have 1,012. 
• At 26 hours, they’ll be back to 1,000 (because that’s when the delta to equilibrium is less than 10).

Let’s look at an example where a user has 500 points (below 1,000, because they’ve given out points to content they like): 

• At 6 hours, they’ll be at 750. 
• At 12 hours, they’ll be at 875. 
• At 34 hours, they’ll be back to 1,000.

Here are four users trending back toward equilibrium over a 24-hour period: 

And here’s a slightly more complicated version of this, where Users 2, 3, and 4 all give User 1  their points throughout the course of a day. 

Every time User 1 jumps up in points, it happens at the expense of another user who has given away their points—and despite accumulating points, User 1 always trends toward 1,000 over time. Despite User 1 continually accumulating points, they trend toward 1,000 over time. The more points they’re given, the faster that trend occurs. The total number of points on the platform is always 1,000 multiplied by the number of users. 

Addressing today’s social media problems

This new model would not be interesting if it didn’t address the issues with today’s social platforms. Here’s why DK addresses them: 

• Follower-based distribution: Unlike the traditional follower model, distribution is based on the short-term perception of the quality of a user’s content. 
• Accumulated advantage: No one is able to sustain their distribution benefit for long without continuing to create high-quality content. 
• Diminishing returns: Feeds are ranked by the quality of the content within them, rather than by engagement likelihood or past accumulation of followers. If errors or inefficiencies are made in DK’s economy, they quickly correct themselves as the platform returns to equilibrium. 
• Echo chambers: Because of the finite number of points in the system, people can only give a finite advantage to those they agree with. In other words, there is no way for a particular group/party/ideology/perspective to gain global advantage over any other. 
• Black-box algorithms: Unlike today’s algorithmic platforms, DK’s logic for distribution is transparent. There is no way to game the system long term, nor can creators gain a long-term advantage without consistently creating high quality content. 

DK is just the start

There are many things one can do to enrich the DK experience—here are just two:

• Encourage users to give out their points so that users with substantial distribution boost smaller creators.
• Allow users to “follow” creators and be notified of new posts. The following mechanism will be left out of global distribution or feed ranking.

The flaws with social media today are symptoms of decisions made in their implementations. If we return to the core question of why we’re building—and if we do it in a way that is inspired by actual economies and markets—we can build something better, more positive, and more meaningful than what we have today.

I love solutions like this, and it’s worth pondering whether you think it might work. There is something useful about an enhanced decay function for any creator’s distribution, and a shift from engagement to points as a way of disincentivizing shallowness and hype. But I have a few concerns:

1. I’d guess that engagement and point-giving are going to be highly correlated. In other words, a user is likely to give points to any content they engage with. There might be some edge cases, especially if point-giving is public, where, for example, I might not distribute my points to gossipy content—but I might spend a lot of time looking at it. But there are many real-world instances where people spend inordinate amounts of actual money on shallow things instead of “quality.” I think that dynamic will persist in this system.
2. Accumulated advantages exist because of the way our brains work, and a decay function doesn’t solve for this. We have a limited amount of attention and can only fit a few creators at a time into our brains. We are also social creatures who look to others to help us filter for what we consume. These combined forces create accumulating advantages over time to a small set of creators. I don’t think a strong decay function solves this problem—in fact, it might incentivize people to race even faster to produce shallow content before their distribution runs out. It’s similar to the dynamic among news organizations to cover breaking stories as quickly as possible before the information’s value decays. 

I love this article because it got me thinking. My two cents is that the best way to solve this problem is to try to put high-quality content out into the world—in other words, be the change you want to see. That’s what Nir is doing with this article, and what we do at Every.

For more on this topic from Every, read Nathan Baschez’s classic,  “Why Content Is King.” —Dan