The Ideal Social Network

Let’s Build A New Social Platform From Scratch

The Idea

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

Here’s how it works. Imagine a platform where every new user is given 1,000 points when they join. As they see content they like, they can allocate some or all of their points to that 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 a state of equilibrium, and it’s the innovation of the community and proliferation of high quality content that prevents that from happening.

A new social experience

For the purposes of this article, I’ll refer to the network as DK (a phonic play on “decay”).

And to address this up front: I’m not interested in building DK. But if you are, reach out and let’s chat.

The Social Networks of Today

Let’s start with the flaws in today’s social networks. We hear of these all the time: echo chambers, toxic commenting, click-bait proliferation, algorithms that support extremism. Social media skews elections, spreads false information, is tied to mental health problems. The list goes on.

And yet the core concept should — at least in theory — work. Technology that brings us together by enabling seamless content distribution across the world. Why does the implementation keep going wrong?

The answer becomes clear when realizing social networks are economies. They have self-serving agents that 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 the laws of 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.

The Traditional Follower Model

First, the traditional follower model (Facebook, Instagram, X/Twitter) allows people to grow their distribution channels and retain them for future distribution. We’re all familiar with these. But we likely don’t often consider four problematic aspects of the traditional follower model:

• Follower-Based Distribution — Distribution is disproportionately granted to people who already have large followings

• Accumulated Advantage — It is much easier for those with large followings to grow their audiences

• Diminishing Returns — As the network grows, feeds become too noisy and crowded by incumbents, resulting in a reduced ability for new creators to break out and succeed

• Echo Chambers — Social connections are disproportionately made between people who think/look/sound alike

Putting these together, it’s apparent why, over time, these social networks feel stale, tend toward extremism, and discourage content innovation. In an economic sense, this is akin to an oligarchy. Some creators come in early, win big, and hoard their channels of distribution.

Real economies have those correction mechanisms I mentioned above. Two examples are depreciation and inflation, economic phenomena that incentivize innovation and capital investments. If a company were not to continually strive for improvement, growth, and efficiency, depreciation of its assets and inflation of prices would drive them out of business in the long term. An ideal social network would need to have similar counter-forces, so that content creators are incentivized to regularly improve and meet the needs of their audience. These would be correction mechanisms.

Furthermore, in the follower model, content quality trends downward over time. Why is that? Consider that as a creator with a large following grows even bigger, they have little incentive to invest in content quality, because they do not run the risk of losing their distribution. Each incremental post need not work as hard as the early ones to derive the same benefit for the creator. Follower retention is significantly easier to attain than follower growth. That’s why monopolies are so at odds with efficient markets: the monopolist grows comfortable, but the market’s correction mechanisms cannot work their magic to counteract that comfort.

The Algorithmic Model

The other type of social network, popularized by TikTok, is the algorithmic model. Here, content spreads based on user signals around engagement. There is less accumulated advantage because a black box algorithm primarily determines which content succeeds and when.

This kind of network fails to be an efficient market for two reasons. First, it prioritizes engagement, not quality. This creates self-fulfilling flywheels, whereby creators are incentivized to create content the algorithm prefers, which only strengthens the algorithm with more engagement-maximizing content.

Second, the black box distribution algorithm is more 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. We’ve all heard stories of media companies driven from high engagement to virtually none on the basis of an unforeseen “change made to the algorithm”.

Both network models (the traditional follower model and the algorithmic model) have structures that create problematic incentives for users, but neither model has sufficient correction mechanisms to ensure that the harmful aspects of social media don’t propagate. Which leads me to…

Introducing DK

The DK product can certainly be iterated on in a number of ways. But for the purposes of this article, I’ll keep it really simple.

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

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

The Feed — The feed is global, and it shows a stack ranked list of creators’ most recent posts based on how many points each creator has. In other words, creators who have created high quality content are given points by others, which ranks them higher in the feed, 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 they can on any other platform (say, text, video, images, etc.). But only their most recent post shows up in the feed at any given time.

Equalizing Force — Every ten minutes, the system slightly reallocates points. Think of is a tax on users with high points that get reallocated to users with fewer points. This algorithm’s goal is to eventually return users to an equilibrium state of 1,000 points each (their starting conditions). This is the “correction mechanism” that markets have, which I mentioned above.

Here’s what the math of the Equalizing Force looks like:

In lay terms: Every ten minutes, an adjustment happens to every user’s points based on how many points that user has. This formula brings a user’s points back to the equilibrium (1,000) at a rate of half every six hours. Think of this 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. So for simplicity, let’s assume each time a user is within 10 points of 1,000, they just return to 1,000.)

Here’s an example. Let’s say a user started with 1,000, posted decent content, and were given points by others. They now have 1,200 points.

• In 10 minutes, they’ll have 1,196 points (4 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 is a graphical look at this, with four users trending back toward equilibrium over a 24 hour period:

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

Notice that every time User 1 jumps up in points, it happens at the expense of another user who has given away their points. Crucially, the total number of points on the platform is always 1,000 multiplied by the number of users. Also, despite User 1 continually getting more points, they still trend toward 1,000 over time. And in fact, the more points they’re given, the faster that trend occurs. You can see this by comparing the slope of User 1 after Hour 12 in the first and second charts above.

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Addressing Today’s Social Media Problems

Of course, none of this would be interesting if it didn’t actually address the issues with today’s social platforms that I mentioned above. So let’s take a look at each of them and why DK addresses them at their core:

• Follower-Based Distribution — Unlike the traditional follower model, here 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 guaranteed to be 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 the economy of DK, 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 because 1) any points given to members of the group come at the expense of other members of that same group, and 2) the feed is global and therefore is guaranteed to contain a variety of content from different perspectives. Recall that the total number of points available is always 1,000 multiplied by the number of users. In traditional follower models, there is no upper limit to the amount of “connections” or “follows” that can be made. DK, conversely, is a zero-sum game.

• Black Box Algorithms — Unlike the algorithmic platforms of today, DK’s logic for distribution is fully known and transparent. There is no way to game the system long term, nor is there a way for creators to be advantaged long term unless they consistently create high quality content.

What’s Next

The platform I described above is only the beginning. There are many things one can do to enrich the DK experience:

• Encourage users to give out their points so that users with substantial distribution “share the wealth” to help boost smaller creators they like

• Create sub-feeds by topic (think filtering by hashtags, for instance) in order to have all of the advantages above contained exclusively within content about a particular subject.

• Allow users to “follow” in the sense of notifying them when creators they like post. But do not allow the following mechanism to dictate who gets global distribution or how the feed is ranked.

• Etc.

I’m encouraged by the idea that the flaws with the social media of today are symptoms of decisions made in their implementations. And if we just go back to the core question of what we’re trying to build and why — and if we do it in a way that is inspired by how actual economies and markets work — we can build something better, more positive, and more meaningful.

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