 Valuing Cryptoassets: How To Do It

The tokens created with cryptocurrencies are fascinating things.

We should not really call them currencies because they do much more than exchange value. They aren’t quite commodities either, since they aren’t necessarily consumable or interchangeable. Neither are they properties or equities, since they don’t generate ongoing investment cash flow.

Rather, “cryptoassets” are an entirely new asset class, and this raises an open question: how do we value them?

In this post, we will propose an answer. We will also walk through the intuition behind it from the ground up, walking through these components:

1. The Equation of Exchange: which offers an underlying framework.
2. Velocity: which shows how the timing of everything makes all the difference.
3. Total Value: which gets to the nature of competition and what drives the value.
4. Holding: which digs into the quantities that people use versus keep.

Understanding the valuation of cryptoassets helps show the transformative impact they might have.

The Equation of Exchange

Most current approaches to cryptoasset valuation start with the Equation of Exchange. It’s a short formula, MV = PQ, that describes how the amount of money in circulation relates to how that money gets used. Here below is what each variable means: The right side of the equation, PQ, is the “demand” for the monies. It then totals up all the activity for which the money is used. The left side is the “supply” and represents the amount of money in circulation that is used.

An Example with Dollars

To show the equation in practice, let’s use an example with dollars.

Let’s say there’s a small economy with just me, you, and ten total dollars in circulation. Then let’s say you buy a book from me for five dollars. Then I buy a book from you for five dollars. And we do this back and forth a total of six times, each time paying each other five dollars for a book. In this case, we have an average price (P) of five dollars multiplied by a quantity (Q) of six books to get 5 * 6 = 30 total dollars spent. With the ten dollars in circulation, each dollar gets used an average of 30 / 10 = 3 times, so the velocity is three.

In equation terms, M (10) × V (3) = P (5) × Q (6)

An Example with “Things”

The formal definition of the equation refers to monies. However, it describes a rule about the use and reuse of things that can apply more broadly. With a few small tweaks we can use it to explain any supply of “Things”: This generalized version helps more clearly show the idea of “demand” on the right and “supply” on the left. The right side, PQ, totals up all the ways in which Things are used — that is, all the activity, or demand, for Things. The left side, MV, describes how many “Things” are available for those activities, and therefore how often they must be reused to meet all of that demand.

An Example with Baseball Cards

With a more general version of this equation, we can apply it to something besides money. For instance, baseball cards.

Let’s say we start a small delivery service that creates a total of twenty special baseball cards. People can use those cards for deliveries through our service, with one card per delivery. And then let’s say that every week, five people show up to make deliveries for 52 weeks. So, that’s 5 * 52 = 260 total deliveries (and thus 260 total card uses) over the course of a year. That’s our PQ, the total activity for the cards. With only twenty cards in circulation, that means we have a V of 260 / 20 = 13 times each card is used.

In equation terms, M (20) × V (13) = P (1) × Q (260)

We can take this one step further and add a price per card. Let’s say the going rate for this kind of delivery is four dollars. This means that if we try to sell cards for more than four dollars, people will go elsewhere. And if we charge less, then too many people will show up, allowing us to charge more. So if we say the market forces of supply and demand level out to a going rate of four dollars per delivery, then the price per card is four dollars.

An Example with “CardCoins”

Now let’s get interesting and create our own currency. Let’s say we call it “CardCoin” and we create 1,000 coins. We tell everyone that they can only pay for deliveries in CardCoins, so they need to first buy the coins. But here’s the catch: we’re not going to set a price per CardCoin, nor a fixed price (in CardCoins) per delivery. Instead, we’re going to let market forces determine those prices.

How does it play out? Well, the most important thing is that we’ve set the supply — we’re introducing 1,000 coins to the market. We’re selling them all and we’ll take the highest price we can get so long as we effectively get the going rate.

On the demand side, those same 5 people show up to make deliveries. When they do, they’ll buy as many CardCoins as possible so long as the price is low enough. If together they buy all of the coins, then each person will get 1000 / 5 = 200 CardCoins. Let’s also assume each person only ever needs to make one delivery, so there’s no reason to hold on to any extra CardCoins . Since the going rate for a delivery is still four dollars, each person is willing to spend up to four dollars for those 200 coins. That sets the value at 4 / 200 = \$0.02 per CardCoin. In other words, when the full supply of coins is sold to the full current demand for them, the going rate per delivery translates into a \$0.02 price per CardCoin.

Going back to MV = PQ, we have a P of 200 CardCoins per delivery, and the same Q of 260 deliveries. That gives us a PQ of 200 * 260 = 52,000 CardCoin uses. With M of 1000, we get V = 52000 / 1000 = 52 times each CardCoin is used.

In terms of the equation of exchange, M (1000) × V (52) = P (200) × Q (260)

This example shows two important points: First is that all of the circulating coins get used by all of the current demand for them. Coins don’t go unused; if we add extra coins to the circulating supply, then the price per coin will fall until they’re all in use. Second point is that the total value of the coins will equal the underlying value that people get from them. If the coins will be used for something worth four dollars then market forces will drive the total value of those coins to four dollars.

Velocity: Changing When People Arrive

The fact that five people show up each week. This timing is critical.

Specifically, what matters is how many people want to use CardCoins at the same time. The level of simultaneous demand is very important because all the coins get distributed across that demand. If the same total people show up over the course of the year, but in differently sized groups, we get different results.

We can see that by adjusting the timing in our example. Let’s say twenty people show up once every four weeks instead of five people each week. We still get 260 total deliveries, but it’s 20 people × 13 periods instead of 5 people × 52 periods. Same deliveries but different timing.

Now the CardCoins get distributed differently. When twenty people show up, they want those same 1,000 CardCoins, so each person gets 1000 / 20 = 50 coins. Since they’re still only willing to pay four dollars for those fifty coins, the value per CardCoin becomes 4 / 50 = \$0.08. All the CardCoins are still used, just in smaller amounts and with a value that adjusts upward to meet the new demand.

Going back to MV = PQ, we have a P of 50 CardCoins per delivery, and the same Q of 260 deliveries. So PQ is now 50 * 260 = 13,000 CardCoin uses. With M of 1000, we get V = 13000 / 1000 = 13 times each CardCoin is used.

In terms of the equation of exchange, M (1000) × V (13) = P (50) × Q (260)

Velocity as a Function of Timing

What we’re seeing is the timing of demand determines the velocity. Smaller, more frequent groups of people speed up the velocity, whereas larger, less frequent groups slow it down more. Intuitively this is because when there are more groups of people that don’t overlap with each other, we can then reuse the CardCoins more often.

There are two ways to quantify this. The first is simultaneous demand:  How many people on average use the coins at the same time. The second is how long the average interval is between each usage. When there are many consecutive groups of people, both the simultaneous demand and the average time between each usage are small. But if everyone shows up at the same time once a year, the simultaneous demand is everyone and the time between each usage is a full year.

Let us now  apply these relationships to our two examples. For both, the total activities (Q) is 260 deliveries and the total time period is one year (52 weeks). For the first example with five people each week, we get a velocity of 260 / 5 = 52 from the left side above and the same value of 52 / 1 = 52 from the right side. And for the second example with twenty people every four weeks, we get 260 / 20 = 13 from the left side and the same value of 52 / 4 = 13 from the right.

Queuing Theory

These relationships introduce the idea of the time between activities. If we can estimate that interval then we can estimate the velocity. Fortunately for us, there’s a whole field of study from operations management that can help: queuing theory.

Queuing theory is the mathematical study of waiting in lines. Using the rate at which people arrive (λ) and the rate at which a server can provide the service (µ), it offers formulas for how much time someone spends in the system on average. To apply this to velocity we can treat our CardCoin example as a queue. People show up to use CardCoins for deliveries, and then servers provide a delivery service.

An important input in queuing theory is the number of servers. With limited servers, it’s possible to show up when they’re all busy, forcing you to wait in line. As you add servers, the average waiting time decreases. And at infinite servers, it’s called an “M/M/∞ queue” and queueing theory proves that the waiting time converges to zero. In equation terms, the average time someone spends in an M/M/∞ queue is 1/μ, which equals the service time. For example, when the server rate is thirty per hour (two minutes per service), the average time someone spends when there are infinite servers is 1/μ = 1 / 30 = 0.033 hrs, or two minutes, the service time.

With cryptoassets we basically have infinite servers since you don’t need to wait in line to start a cryptocurrency transaction. So we’ll use the M/M/∞ queue equations going forward. For velocity, we want the average time between activities, and we now know the average time in the system is the service time, 1/μ. This means that if all the activities were to happen back-to-back — that is, there were zero gaps between them — then the average time between activities would equal the service time. That would give us a velocity of 1 / (1/μ) = μ.

However, we can’t assume we’ll have zero gaps. No matter how high the demand is on average, there’s always some chance that for a moment there could be no demand and that the system could be idle. That would then increase the average time between activities and decrease the velocity.

We therefore need to refine our velocity equation to account for those gaps when the system is idle. To do that we can subtract the percent idle time from the numerator, total time. Combining that with our μ value above, we then get a refined formula.

Idle Time

The last step is estimating the percent of idle time. Another way of phrasing this is the probability that zero people are in the system. Conveniently, queuing theory derives this value, π(n), which is defined as the probability that there are n people in the system once reaching a steady state. The mathematical proofs get complex (here’s a great resource), but we can skip to the final M/M/∞ Let’s apply some real numbers. Let us say the arrival and server rates are equal, i.e. people show up ten times per hour and the servers can also serve them ten times per hour. In this case, the percent idle time is e^-(10/10) = 0.368, or 36.8%. This means that when the arrival and server rates are equal, with infinite servers, the whole system is expected to be idle 36.8% of the time. If the arrival rate is double the server rate, than the idle time is e^-2 = 0.135, or 13.5%. And at triple the server rate, we get e^-3 = 0.050, or 5.0%. As the arrival rate increases relative to the server rate, the percent idle time converges towards zero.

With this last piece we can thus update our velocity equation. In this context, the arrival rate, λ, is the number of activities per year, Q. therefore, we get this equation: Back to CardCoins…

To see our new equation in practice, let us now go back to our CardCoin example. We still have 260 total deliveries, but this time let’s say the timing is random over the course of the year (which the above equation does). Let us also assume we now have infinite delivery drivers, meaning no one ever waits in line - and that on average each delivery takes just three hours.

Let’s first consider the maximum possible velocity, when the idle timing is at a minimum. This happens if none of the deliveries ever overlap, allowing us to reuse all of the CardCoins every time and thereby get a velocity of 260. Out of 24 * 365 = 8,760 total hours per year, we would use the coins for 260 * 3 = 780 hours worth of deliveries. That results in 8760–780 = 7,980 hours of idle time, or a percent idle time of 7980 / 8760 = 91.10%.

That’s for the maximum possible velocity, but we know that deliveries might overlap and thereby decrease the velocity. This is where our new equation comes into play. At three hours per delivery, our μ is 8760 / 3 = 2,920 potential deliveries one server can do per year. With our Q of 260, we get a percent idle time of e^-(260/2920) = 0.9148, or 91.48%. It’s slightly higher than the minimum possible 91.10%, accounting for the probability of deliveries overlapping. Accordingly, the lower velocity will be 2920 * (1 — e^-(260/2920)) = 248.8.

To see how the numbers change, consider a new example where deliveries take a full day instead of 3 hours. It’s still technically possible to get that 260 velocity, but it’s much less likely because the deliveries would need to line up almost perfectly. There’s a higher probability that deliveries overlap, so the velocity should be lower. Using our equation, our new μ is 365 potential deliveries per year and our new velocity is 365 * (1-e^-(260/365)) = 186.0. As expected, the velocity is much lower, reflecting the higher probability of deliveries overlapping.

Holding aka Hodling

The Effects of Holding

Throughout these aforementioned examples, we’ve assumed everyone uses all of their CardCoins once they get them. It’s possible, though, that they’ll hold on to some of them. When they do, that affects the results because it decreases available supply.

To take into account the idea of holding, we can say that coins being held are not really in use. Let’s say N is the total supply of Things or coins. We can then define our M, the number of them actually being used for activities, as the total minus the percent being held:

To see how this changes the results, consider our CardCoin example where twenty people show up every four weeks. With the original supply of 1000 StampCoins, each person used fifty coins and the value per coin was \$0.08. Now let’s say that 20% of the coins are being held. Our new M is 1000 * (1–0.2) = 800 coins. Those 800 coins then get distributed among the same 20 people, so they each get forty coins instead of fifty coins. They’re still willing to pay up to \$4 for their coins, so the value becomes 4 / 40 = \$0.10 per CardCoin. The value increases from \$0.08 to \$0.10 due to holding.

To dig into the holding percent, it helps to think about a real life example like regular baseball cards. Generally, we don’t stockpile cards. We can’t do much with them besides trade them, so it doesn’t make sense to buy too many extras.

There are two reasons, though, why we might in fact get extras: a) it’s cheaper and easier to do so and b) we think they’ll retain value, like an investment. Understanding the potential value is, of course, the ultimate goal of this analysis. First we need to cover a), why holding might be cheaper and easier.

Consider that even if we don’t stockpile cards, we might still buy a few extras to keep in a drawer. It doesn’t cost much to leave them there. In other words, holding is easier. Yet we might not buy a whole batch all at once because that could get expensive. It makes sense to save a few… There’s a balance.

Finding the right balance is about minimizing two costs: the cost of going out to get more cards (the “transfer” cost), and the cost of keeping extras (the “holding cost”). The optimal amount to get at a time will minimize the total of those costs.

The field of inventory management has a term for this optimal amount. It’s called the economic order quantity (EOQ), and it generally applies to physical items with holding costs like warehousing. In the case of coins and “cryptoassets”, we don’t really have physical holding costs, but we do have the opportunity cost of keeping those funds somewhere else where it can earn interest. So we need a refined version of economic order quantity that uses the interest rate as the holding cost. This is known as the Baumol-Tobin model.

The Baumol-Tobin model derives a formula for how much cash we would want to withdraw at a time.  In our example, how many CardCoins we’d want to buy … given the total amount we want to eventually use. It addresses a similar trade off: withdrawing too much upfront loses out on the potential interest, however making many smaller withdrawals comes with additional transfer costs.

The resulting formula for the optimal withdrawal amount is based on the fixed transfer cost, k, the total amount we end up spending, Y, and the interest rate, i: We can see what it says at a high level. The transfer cost is in the numerator, so we’ll withdraw more at a time when the cost is higher. This makes sense because we’d want to reduce the number of withdrawals. The interest rate is in the denominator so we’ll withdraw less when the rate is higher. This also makes sense because we would want to leave more money wherever it’s earning that higher interest. Together, the formula balances these two forces to get the optimum amount.

Let’s apply it to our CardCoin example, this time assuming we’re a customer who makes eight deliveries per year. Our Y is \$4 * 8 = \$32 worth of deliveries. Let’s also assume the transfer cost is \$0.50 every time we exchange dollars for CardCoins, and the interest rate we could otherwise earn is 2.00% per year. Plugging in these values, we get sqrt((0.5 * 32) / (2 * 0.02)) = \$20. To minimize costs, we’d therefore want to buy twenty dollars worth of CardCoins at a time.

Given an optimal amount we need to translate this into a holding percent. In general terms, the holding percent is the average amount we hold at a time, H, divided by the total of both what we hold and the average amount we spend per activity, y. That thus gives us the following equation:

To determine the average amount held, consider the above example. We’ll buy twenty dollars worth of CardCoins and use them up. Then we’ll buy another twenty dollars worth, then back to zero, and so on. Our average holding amount will be in the middle, ten dollars worth, or half our optimal amount. We can therefore divide the Baumol-Tobin equation by two and plug it in for H to update our equation below: Now we can apply this equation to the numbers in our example. With our Y of \$32 and our y of four dollars per delivery, we get a holding percent of 1 − 1 / (1 + sqrt((0.5*32) / (8*0.02*4²))) = 0.71, or 71%. Double checking our math, we hold ten dollars on average and spend four dollars per activity, so we get the same value with 10 / (10+4) = 71%.

Finally, we can plug this into our equation for M to get the following:

Total Value:

In all of our CardCoin examples so far, we’ve worked with the assumption that the “going rate” per delivery is four dollars. This is an important detail. Ultimately many market forces balance out to determine this rate. With cryptoassets the primary driving force is supplier competition. So we can focus on that.

Firstly, consider the effects of competition in general. As a supplier, we’ll charge the highest price we can. Competition then adds an incentive for us to lower said price in order to win over customers. The higher the competition, the more pressure we will have to charge less and thus accept lower margins. That is especially true when our offerings are similar, leaving price as the main differentiation. As competition increases, the price converges toward the costs. Eventually, with infinite competition, the price will equal the cost.

With cryptoassets, we essentially have infinite competition. This is because it is easy to introduce alternatives, even exact replicas. The barriers are low for several reasons:

• All of the software is public. To offer an alternative, you can literally copy and paste the code.
• All of the data is public … If the fees are too high, the protocol can be forked into a new version where the same users can still use their same addresses and passwords, but pay lower fees.
• Trust is built into the system. There’s less of a need to develop a new, trusted reputation because the code and the network are transparent.
• There are minimal capital costs required to get started. Cryptoassets have built-in financial incentives for other people to run the network, so you don’t need to pay for the computing or hardware costs yourself.
• The friction between options is low. Since cryptoassets are based on code, it’s possible to interact between them. The process of switching from one to another can therefore be scaled and automated with software, minimizing friction.

Together, these elements remove the competitive advantages that generally allow businesses to charge a premium. With no proprietary tech or data, plus low costs of entry and switching, there’s little to block new competitors. One missing element from this list is branding:  one could argue that well-known cryptoassets might lean on their name recognition to command a higher rate. But with trust already built into the protocol, it is hard to imagine being able to charge a premium in the long term simply because of your name. Generally, then, there isn’t much stopping a new competitor from entering the market and charging less.

The Total Cost

Without any barriers to competition, the competition is essentially infinite. A cryptoasset will face downward pressure on the value until it equals the cost of running that cryptoasset.

Going back to MV = PQ, remember that both sides of the equation total up all the uses of things or coins. The total value of those uses will therefore equal the total cost of them. To express this value in terms of U.S dollars, we need an exchange rate, A, the dollar value (i.e, price) per Thing. Multiplying both sides by the price, we get A*MV = A*PQ. Both sides now equal the total U.S dollar value and therefore the total US dollar cost for all of the activities. Replacing the right side with a single variable for this total cost, C, we get a new equation: When we apply this idea to CardCoins, this cost refers to the cost of making all of the deliveries. That likely includes fuel, vehicles, and labor. We then total up those costs across all of the deliveries in a year to get the value of C. Notably, it doesn’t matter what’s in the delivery package — it could be empty or contain a million dollars, and the delivery cost will still be four bucks.

Applying this to cryptoassets, let us say we have a cryptoasset that runs a messaging network. In this case the network’s activities involve delivering messages. So the value of C will equal the total computing, electricity, and storage costs for handling all of those messages. It likely includes labor costs as well, reflecting whatever minimal margins are necessary to incentivize devs to keep things running.

With cryptoassets that handle payments, it gets a bit less intuitive. If we treat a cryptoasset like a currency and send you five dollars worth of that cryptoasset, how does that five dollars factor into the equation? The answer is that the five dollars is part of the cost. Let’s say that in general, the computing costs are \$0.01 per transaction, where “transaction” loosely refers to any activity on the network. If we’re sending a message, then we only need \$0.01 worth of the cryptoasset to do the transaction. But if we’re sending five dollars worth of a cryptoasset, we need \$5.01 worth to do the transaction.

Intuitively, this goes back to the idea of how much we’re willing to pay for the coins we’ll use for an activity. With CardCoins, were willing to buy up to four dollars worth of coins to do a delivery. With messaging, we’re willing to buy up to \$0.01 worth of coins to send a message. And with a five dollar payment, we’re willing to buy up to \$5.01 worth of coins to make that payment. Payments are therefore a special kind of transaction where the content of the package affects its cost. But once we factor in that cost, the rest of the calculation is the same.

A Store of Value

This cost-based value is often described as the “utility” value. The utility of a cryptoasset means the services it can perform, i.e storing data, verifying info, and executing network requests. In other words, doing activities. So our discussion so far has all focused on the value a cryptoasset has because of its utility.

There’s also a second potential source of value, of serving as a “store of value”. A store of value is something we essentially all just agree has value, and that we’re therefore willing to save and exchange with each other. As with art or fiat money, the value doesn’t depend on underlying utility, but rather on our mutual trust that it will keep serving as a store of value.

Because it relies so much on trust, it is pretty speculative how much cryptoassets may end up serving as a store of value. I’m unaware of any economic models that point to a good answer. One common approach, though, is to consider what would happen if a cryptoasset were able to match gold as a store of value. To be fair, it’s still anyone’s guess, but I think it’s a reasonable comparison. So let’s walk through this approach.

The calculation itself is straightforward: we take the total value we expect the cryptoasset to store and then divide by the number of coins. For this approach, we want the total value of gold. According to Wikipedia there are 62.5K tonnes of gold in the world excluding utility uses like jewelry and industrial materials. With the recent spot price of about \$1,350 per ounce, or 1350 * 35274 = \$47.6M per tonne, we get a total of 47.6 * 62.5 = ~\$3.0 trillion worth of global gold holdings. Then we divide by the number of coins. For bitcoin, if we use the eventual total supply of 21M, we get 3.0T/21M = ~\$140,000 per bitcoin.

If cryptoassets serve as a store of value, it could significantly increase the valuations. Another more detailed approach also considers how cryptoassets might displace non-gold international reserves; that reaches even higher estimates in the \$5-\$15T range, versus \$3T. But this starts moving into speculation territory rather than economic theory.

Bringing It All Together

We now have all the pieces. Taking our above equation for total cost, we can solve for the value of a cryptoasset, A, to get A = C / (MV). We can then plug in our equations for M and V from the prior sections to produce one big equation: One final update is that we can now express y, the average spent per activity, as the total cost divided by the total quantity, so y = C / Q. Substituting this in, we can then rearrange the variables and remove the labels to arrive at our final equation which is: Example Calculations:

Let us now try some calculations for potential real-life utilities. We’ll use Ethereum since it’s currently the main smart contract platform, and we’ll start with these general inputs:

• N — The total supply of Ether (ETH) is about to reach 100M.
• i — We’ll assume 2%.
• μ — We’ll assume transactions take fifteen seconds, which means μ = (365*24*60*60) / 15 = 2,102,400. This is slower than current transactions, but practically speaking it’s about the max acceptable time for real-life utilities like a payment system. Either Ethereum would need to achieve this speed, or another faster cryptoasset would probably end up providing these utility services.
• k — Many exchanges charge fees on a percentage basis, which don’t affect the holding percent. But some do have minimums. We’ll assume a fixed (minimum) cost of one dollar for exchanging USD for ETH. And for exchanging ETH for other cryptoassets, the costs are usually lower, so we’ll assume \$0.10.

Example: Ethereum executes all VISA payments

According to the annual report, VISA executed 111.2B transactions last year, so this is our Q. Those payments were worth \$7.3T, our C. (We need to add in operating expenses too, but at only ~\$6B, they’re less than a rounding error of the \$7.3T.) With 3.2B cards, the average annual spend is 7.3T / 3.2B = ~\$3,200 per card, our Y. Let us also assume people buy their ETH with U.S dollars and use the higher one dollar transfer cost.

ETH Value: (7,300,000,000,000 + 111,200,000,000 * sqrt((1.00*3,200) / (8*0.02))) / (2,102,400 * 100,000,000 * (1 — e^-(111,200,000,000 / 2,102,400)))
\$0.11

Example: Ethereum delivers all global emails

study from 2015 estimates that 246B daily emails will be sent globally by next year. That equals roughly 90T annually, our Q. Amazon’s email service charges \$0.09 to \$0.12 per 1,000 emails, plus a fee of \$0.12 per total GB. If we assume a net \$0.15 cost per 1,000 emails, the total cost is 90T * (0.15/1000) = \$13.5B, our C. The study also estimates those emails will be sent by 2.9B users, so the average annual spend per user is 13.5 / 2.9B = \$4.66, our Y. We’ll also assume ETH is being exchanged with other cryptoassets and use the lower \$0.10 transfer cost.

ETH Value: (13,500,000,000 + 90,000,000,000,000 * sqrt((0.1*4.66) / (8*0.02))) / (2,102,400 * 100,000,000 * (1 — e^-(90,000,000,000,000 / 2,102,400)))
\$0.73

Example: Ethereum handles all Dropbox storage

The recent Dropbox IPO filing states that 2017 cost of revenue excluding stock compensation was \$356.7M, our C. Since files can take longer to upload and download, we’ll say transactions average 30 seconds. That means μ = (365*24*60*60) / 30 = 1,051,200. With over 400B pieces of content, let’s assume annual transactions is 10% of that, 40B, our Q. And with 500M registered users, the average annual spend per user is 356.7M / 500M = \$0.71, our Y. We’ll again assume ETH is exchanged with other cryptoassets and use the \$0.10 transfer cost.

ETH Value: (287,200,000 + 40,000,000,000 * sqrt((0.1*0.71) / (8*0.02))) / (1,051,200 * 100,000,000 * (1 — e^-(40,000,000,000 / 1,051,200)))
\$0.00026

Conclusion: What It All Means

The first thing that’s clear is this equation produces low valuations. Across the different examples of potential utility services, the values are all under a dollar, a small fraction of current prices. You might expect cryptoassets to gain value with adoption, but this equation suggests the opposite. At scale, cryptoassets will likely achieve hyper efficiency that reduces the price.

A big reason for this is velocity. Through the sheer volume and speed of transactions, it’s possible to reuse the coins quickly. Compared with the U.S dollar’s velocity of 5 to 10 and the U.S stock market’s velocity of 150 to 300, the above examples see cryptoasset velocities exceeding one million. A second reason is that competition drives down the cost by limiting profit margins. With low cost and high velocity, cryptoassets can be remarkably efficient in their use of capital and thereby reach lower values.

This would hardly discourage activity, however, but rather expand it. A substantial amount of money would flow through the system, and increasing with scale. The equation therefore suggests it would become a commoditized network that processes all of that activity while claiming only a slim margin — which, on billions of dollars in operating costs, is still pretty good. That leaves ample financial incentive for developers to build the network and keep it running.

And for the economy in general it presents a major opportunity. This kind of system could basically offer public versions of any service capable of automation, like a set of open and near-free APIs. That would create a new layer of functionality upon which new businesses would become economically viable. Admittedly it’s difficult to predict now what those will be. What is clear is that cost efficiency could pave the path.

Which is all to say that the cheapness of cryptoassets could very well become their main selling point. Folks often describe the impact of blockchain tech in terms of decentralizing power, but after this analysis, we think the bigger impact could be economic. If the equation holds up, it suggests that commoditized cryptoassets could form a utility foundation for a new class of industry.

Olé Crypto,
CBNN

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