Blockchain is more than code – it’s digitized economics.
Protocols like Bitcoin, Ethereum, Cardano, Filecoin, Zcash, etc. use economic incentives to monetize the value of the work performed on their network. With the right incentives, the token or cryptocurrency then acts as a conduit to monetize that value.
But to navigate the 16,000-plus cryptocurrencies flooding the internet today, we need a framework to think about that value. And a few years ago, I laid out a couple of approaches in an article that I’d like to share with you today (the original can be found here).
Not long after I published this piece, I got a surprising text from a friend. He was attending the World Economic Forum in Davos (yes, that one) and he said in his text, “Hey man, I’m here in Davos. I just got out of a session about crypto and someone mentioned your article. A lot of people had read it. And we spent a long time discussing it.”
Though I’m not really the Davos type, I’m glad it created some food for thought somewhere.
And I hope it does the same for you too…
True economics is the study of human action.
And good token models acknowledge the power of bottom-up economic activity.
That I spent twenty-three years in investment management modelling value factors and questioning the top-down economic dogma of the industry from the perspective of classical liberal economics seems to have helped me understand the blockchain space (at least permissionless systems). However, regardless of the domain, these are still businesses started by individuals with profit motives. As I talk to these entrepreneurs, they often get stuck on how they can benefit financially from doing business on blockchain.
To be sure, cryptocurrency and blockchain technology is not driven by people with purely altruistic motives.
They may have motives distinct from profit, but they all have strategies to benefit financially. Ultimately, they realize there are different ways to generate and capture value.
By building networks that incentivize certain types of work, they monetize the value of the work performed on the network — the token or cryptocurrency is how they monetize that value. For the typical entrepreneur, this requires a very different perspective on revenue and profit models.
With crypto, there are two fundamental models at work. One relates to the Quantity Theory of Money and the other relates to Network Theory of Value.
The Quantity Theory of Money is a fundamental approach that can be used to explain a token’s intrinsic value. The Network Theory of Value effectively explains its total value, which can drive a premium to the fundamental value.
Let’s start with the fundamentals.
The Value of Money.
The Quantity Theory of Money has been around for decades and, when I started in this business, it was still relevant to analyzing monetary policy (though it definitely fell out of favor over the last three decades).
It states that the total money supply (M) multiplied by annual turnover of the money supply, or velocity (V), equals the price of the average goods and services (P) times the total quantity of goods and services purchased during the year (Q).
Thus, MV=PQ. MV is the total quantity of money demanded over a period of time and PQ is the total value of goods demanded over that same period. Traditionally, PQ is nominal GDP where P is the level of the inflation index and Q is real GDP. M is a measure of the supply of U.S. dollars in circulation, for instance, and V is the annual turnover of the dollars required to meet demand for the dollars to buy goods and services.
Now let’s look at a token that represents a unit of data stored or otherwise linked to a blockchain record. In the case of this token, M is the total circulating supply of tokens (only issued, not held in reserve or otherwise locked out of circulation), P is the number of tokens required to purchase a unit of data (think of it as the average price of a unit of data across the system), Q is the total units of data exchanged on the system during the course of a year and V is the number of times the token supply turns over during that same time period.
Note that P is inversely related to token value. If today one token buys one unit of data, then a holder of the token can command one unit of data. If tomorrow it takes two tokens to buy one unit of data, then the holder of one token can only command a half-unit of data, which is a depreciation in value. Alternatively, if tomorrow it takes ½ a token to buy one unit of data, the holder of one token now commands two units of data, so his token has appreciated and is now more valuable.
We can rearrange the formula to get P on one side of the equation, so MV/Q = P. P will decrease if our token appreciates in value so the incentives should be the following:
- Keep M (the total supply or tokens in circulation) relatively low
- Keep V (annual velocity) low
- Drive Q (the total quantity of data demanded) higher
Incentives within the system should be aligned accordingly.
Staking, burning or recycling (circular model for tokens) all help minimize M. Increasing the quantity of high value data increases Q so there should be mechanisms and/or incentives to validate the quality and to contribute data — this is where curation models come in. Velocity is really about expectations of increasing value and if people expect the value to increase, they will hold it rather than exchange it.
Do all of these things right and grow your network, then P will decline, and the value of the token will increase.
And that’s when the second driver of value kicks in.
The Network Theory of Value is based on Metcalf’s Law, which states that the value of the network is related to the square of the number of users (U).
To Metcalf’s law has been added the number of transactions, though there are many ways to estimate transactions. I describe my application of the model in the article linked here, where I use average number of token transactions per user (address) priced in USD (Tx) as my measure of transactions. To model the total market value of a token (total token supply * value per token in USD), or token market cap (MCT), we get the following:
MCT = U^2 * Tx
The implication is simple: increasing either the number of users (as measured by addresses) or dollar value of each transaction drives up the value of the token. The value implied by the network model can easily exceed the intrinsic value of the data on the system, which can be thought of as speculative value. Whether it’s pricing the present value of the future state of the data or simply a greater fool dynamic among traders is irrelevant. More people trading the token means more addresses and higher dollar value transactions per address, which drive the network effect higher, even if artificially above actual value of transactions for data on the chain.
So, as founders of the data ecosystem itself, there isn’t a revenue model per se, but there is a profit model. Sell tokens from reserves (or held by founders) gradually over time as the US dollar price of the token appreciates significantly beyond the value of the data within it.
As for revenue, there should be many actors within the system hoping to profit, such as data contributors, data analysts, data market makers, etc., and their revenue models will be to earn tokens. The more they hold onto these tokens rather than exchange them immediately for dollars, the better (see Velocity discussion above). Founders can then also earn revenues by contributing to the system going forward.
I personally believe that founders should avoid taxing the system directly to the benefit of any single actor (which may be defended as a formal revenue model), as it violates a principle of permissionless, open-source token economics. The monetization is a result of building the right incentives and profiting from token appreciation. I believe revenue should be derived entirely by competition on the chain. Otherwise, it feels like an entitlement that represents an unfair advantage against other players and could disincentivize their involvement. The incentive to drive the value of the token higher is simple and straightforward.
Thus, in this case, to model the total value of the system you estimate how much data you can get on the chain, the value of that data and the number of times that data turns over, or is demanded, as several players can demand the same data set. Then you estimate the growth of the data set. Given the size of the data market and value of that data, the numbers can quickly get staggering.
But for a founder confident they can craft solid incentives and build a robust network of users and contributors they may just have a solid blockchain business.