This short example illustrates how easy it is to misjudge the future cryptoeconomic dynamics of a blockchain network. It illustrates a frequent mistake we observe with a model we call “USD-denominated pricing.”

I call a transaction USD-denominated when it is carried out in platform cryptocurrency, but denominated in US dollars. This means that the network will price transactions according to the price at which the platform currency trades on the current cryptomarket.

Let’s take an example. Suppose a network provides a decentralized service (such as file storage or computation) and one unit of service is called S. S could be, for example, “one megabyte of storage per day” or “one gigaflop of computation”. Suppose the network is funded by issuing a platform cryptocurrency S-Coin, which is then intended to be the currency used for purchasing the network’s service.

Imagine the network functions as follows. A set of oracles report the current dollar price P of S-Coin to the network every once in a while based on current exchange data. The network also has a parameter set by some collective means of how much each unit S costs to buy. The parameter C (cost) is expressed in USD. When a customer goes to buy service from the network they will be charged a payment V = C / P of S-Coins per unit of service purchased. So if P is 2 (S-Coin costs $2 on an exchange) and C is 10 (the transaction costs $10) then V = 5 (the customer will have to pay 5 S-Coins).

On the other side of the transaction sits a member of the network (a miner, an owner of a CPU array, or an owner of a hard drive) who gets paid from the network’s transaction fees.

This sounds like a reasonable model so far. Indeed, the network creators may be inclined to do this because they want the network to receive a fixed guaranteed price per unit of service that does not depend on the market price of S-Coin. The creators may also reason as follows: our network provides a useful service and as the network acquires new users the price of S-Coin is going to increase. At token offering they express this premise and issue a pre-allocation of S-Coin to investors in order to get development funding for their network.

Let me illustrate the problem with this statement and the reason why it is entirely unclear what the long-term behavior of S-Coin is going to be. In fact, the price behavior of S-Coin in this model is entirely speculative and has very weak ties with the degree of adoption of the network.

It is true that a user of the network will have to acquire S-Coin to purchase the service. But the S-Coin that the user pays to the network then goes to a different party, who may be inclined to sell S-Coin immediately. The overall market impact of the customer’s purchase of S-Coin is then negated by the service provider’s sale of it.

You can look at it this way: the market price of S-Coin depends on market liquidity. If users are inclined to hold S-Coin, liquidity is low and customers who want to buy service will move the market up, while service providers who hold it rather than selling it right away do not negate this effect. But if users are inclined not to hold S-Coin (due to, for example, market risk or negative sentiment) then the opposite is true and a market flooded with cheap S-Coin will have a very negative price trend irrespective of how much the network is used.

Because the network’s transactions are USD-denominated, the network functions well in both cases, as transactions will always cost the same in USD terms. But the statement that the price of S-Coin is somehow dependent on adoption is not at all true. The fast velocity of money in the S-Coin ecosystem (here, velocity refers to how often the same coin is spent) will drive S-Coin prices down while speculative sentiment (inclination to hold S-Coin) will drive prices up.

We often hear investors put too much stake on the relationship between adoption and cryptocurrency price. In truth, you have to look at economics very closely in order to understand whether such relationship even exists. Developers of decentralized networks often don’t evaluate these dynamics correctly and inadvertently represent to their investors the expectations that are simply not true.