I don’t like talking about price. Discourse around price is largely driven by speculators (who I have been dismissive of in the past) that make it noisy, misleading, and distracting for projects and believers with longer time horizons.

But price has an unquestionable impact on the composition of individual cryptonetworks and the crypto industry as a whole. Trying to understand how to build and manage cryptonetworks without modeling for price would be like building a plane without considering wind because it’s more “pure” to focus only on the mechanics and physics of the plane in a vacuum.

Euphoric bull markets attract speculators, scammers, and click-bait, but also leave us with serious projects and true believers that may not have been exposed to crypto otherwise. Serious market corrections weed out the bad actors but reduce the pool from which a project can hire or find users.

In this post, I explore a conceptual framework to understand the relationship between the price and the composition of a cryptonetwork. It draws from reflexivity–a concept popularized by George Soros–and startup growth concepts like activation and churn.

Key concepts

First, let’s establish a few key concepts that form the basis of my model.

Fallibility and reflexivity

I can state the core idea in two relatively simple propositions. One is that in situations that have thinking participants, the participant’s view of the world is always partial and distorted. That is the principle of fallibility. The other is that these distorted views can influence the situation to which they relate because false views lead to inappropriate actions. That is the principle of reflexivity. –George Soros1

Reflexivity, a concept popularized by George Soros, explains how perception can impact fundamentals and vice versa. This is “sheer common sense,” but significant because of the idea that information can be imperfect and influence fundamentals had not been studied in economic theory.

An example of reflexivity is “treating drug addicts as criminals creates criminal behavior [because] it misconstrues the problem and interferes with the proper treatment of addicts.”

Soros’ concept is particularly helpful in crypto, where information is highly imperfect and a consensus valuation framework has yet to be created2. During Ethereum’s bull run in April of last year, Joshua Seims at Metastable Capital wrote:

Cryptographic tokens have the properties of money, in some ways better than any previous forms of money. And humans commonly imbue tokens with monetary value through belief. By connecting these two, we create a feedback cycle. More belief leads to more utility leads to more belief, etc. To use the George Soros’ Theory of Reflexivity, a rising price for monetary commodities increases the utility of the commodity, thereby increasing demand for the commodity, creating a positive feedback loop.

Indeed, the next year would prove the efficacy of this cycle. Price attracted droves of speculators that were heartened by continued increases in price, continuously pumping this cycle.

But, as we’ve seen, reflexivity can be applied in the opposite direction.

As Jonathan Cheeseman and Chris Burniske wrote in April of this year:

Turning to the current bear market, we can divide the loss in network value thus far in 2018 by the estimated tax liability outflow, to define a maximum fiat amplifier. Dividing the $590bn drop from early January highs to present, by our estimate of $14bn net outflows, yields a maximum fiat amplifier of 42x.

They define a “fiat amplifier” to represent the reflexivity of the cryptomarkets. A fiat amplifier in the positive direction takes one dollar worth of fiat inflows and returns X dollars of increased market cap. In the negative direction, one dollar out leads to X dollars of decrease in the market cap.

Perhaps the best example of reflexivity leading to decreased prices is Ethereum. While I believe the concern around ICO projects being forced to sell their ETH-denominated treasuries has been overstated, it’s a useful and easy to understand example.

As Tetras capital wrote in their Ether Bearish Thesis:

Investors use ETH to participate in ICOs. As ETH price declines, two things happen: Ethereum ICOs become less attractive ventures ICOs that hold ETH lose runway These are reflexive. If ICOs are a major source of ETH demand, then: Decreasing ICO demand hurts ETH price Projects that fundraised via ICO contribute more sell pressure as they diversify holdings to minimize loss (from holding ETH) and lock in a static capital base

On the way up, the market structure for ETH benefited from reflexivity (just read the Tetras excerpt in reverse). On the way down, it catalyzed a price unwinding.

The growth funnel

The second concept is the growth funnel or growth loop3. There are many ways to represent this concept, but the intention is to model the process of acquiring, activating, and retaining users.

Acquisition: attract a new user Activation: convince the new user to take some action Contribution: get the activated user to produce a contribution to the network

All the while, you want to prevent churn, where a user decides to drop out of your network. This can happen at any stage.

Here’s an example from my blog:

Acquisition: a reader shows up on this post after seeing it on Twitter Activation: the reader likes what they read, so they decide to become a member Contribution: the new member tells their friends about it, leading to another new reader

Hopefully, this reader does not churn by deciding not to renew their membership.

Simple enough, right? Now let’s get to the framework.

The Coin Price Flywheel

The Coin Price Flywheel offers a conceptual framework for understanding the impact of price on the composition of a cryptonetwork. The direction of the loops are based on reflexivity, and the transitions from one step to the next are inspired by growth funnels.

Flywheel up

Here’s the flywheel in action as the price increases:

The price of a cryptocurrency rises

Attracting speculators

Some of whom convert into actual users

Increasing belief in the cryptocurrency

Driving up the price of the cryptocurrency

And so on. When the flywheel stops spinning and how fast it spins depends on a lot of variables. Each stage could justify a series of blog posts.

But a useful way to think about the progression through the flywheel is a growth funnel or growth loop. The flywheel is more or less effective based on how well you convert at each stage.

What determines how many speculators are acquired due to a 1% rise in price? This is a question of acquisition. Better, more trustworthy user experiences like more fiat-onramps and even regulated stablecoins improve the rate of acquisition.

What percentage of speculators turn into actual users? This is activation. Educating and motivating speculators to actually use a cryptocurrency–even if it’s just to hodl–determines the percentage of speculators you can turn into users. My post “Don’t design for speculators” is really about optimizing for activated users.

How does belief turn into prices? This is fueled by The Narrative Bubble Loop.

This transitions of this flywheel give stakeholders tangible ways to improve a cryptonetwork. The better the transitions, the more effectively you can bootstrap the network.

Flywheel down

On the other hand, the flywheel spins in reverse when the price goes down.

Dropping price diminishes belief

Decreasing users

Scaring speculators

Who dump the coin

Decreasing the price

And so on. Just like before, the transitions are the key, but in this case, you want to reduce the rates at each transition.

The strength of the mass movement around your network dampens the impact of price on belief. You see this with the Ethereum developer community. A massive drawdown in price can’t be felt at ETHGlobal developer meetups.

Churn from users and speculators depends on how useful the cryptocurrency actually is. If the coin’s utility is a contrived velocity sink, the users will have a low tolerance for holding during a downward flywheel. But if it’s part of their daily lives, something they value greatly, or if there are no better options, then the churn rate will be lower.

Clearly, the motivation for stakeholders here is to dampen reflexivity on the way down. While there are many factors that can impact the reflexivity of a cryptoasset (e.g. market manipulation, fragility of the market structure), I think churn is a reasonable place to start.

Reflexivity is proportional to churn which is proportional to the ratio of speculators over actual users. So if the composition of your network is mostly speculators, you’re more likely to see an amplified Coin Price Flywheel on the way down.

Why is this? My assumption is that given an ecosystem where you’re either a speculator or a user, the speculator adds more reflexivity than the user because the speculator is less likely to have accurate information (fallibility) and is more likely to both have their perception influenced by price and want to manipulate the price (reflexivity).

Conclusion

The Coin Price Flywheel model helps us understand how price movements fuel loops that impact the “fundamentals” of a cryptonetwork. Rather than follow the conventional wisdom to ignore price, projects would benefit from managing the transitions in the flywheel to best benefit from positive flywheels and dampen negative flywheels. Converting speculators into users seems most important of all. It helps bootstrap actual usage on the network when the price goes up and dampens reflexivity when the price goes down.