Highlights the Velocity Problem for utility tokens, makes the comparison of utility tokens to working capital and proposes that any crypto asset that is not viewed as a store of value will have a difficult time accruing value over the long-term.

It’s striking how similarly most “good” token models operate. They are fundamentally productive assets, some of which resemble securities. Discount tokens, profit sharing tokens, work tokens, and burn and mint tokens are built on an assumed margin or fee that is distributed to the service provider or token holder. With these value accretive tokens, simple net present value formulas can be used to reasonably estimate a token price.

A formative thesis on the investment opportunity set of the crypto asset landscape.

Despite the incredible amount of attention and material written about cryptocurrency tokens, there hasn’t been a good mainstream definition of what they are. In the technical realm of the blockchain, the concept of a cryptocurrency token is well understood. It represents a programmable currency unit that is bolted to a blockchain, and is part of smart contract logic in the context of a specific software application. But in the non-technical arena, what is a token, really?

A token is just another term for a type of privately issued currency. Traditionally, sovereign governments issued currency and set its terms and governance; in essence directing how our economy works with money as the exchange medium for value. With the blockchain, we now have new types of organizations (and soon, more of the existing type) who are issuing their own currency in the form of digital money as cryptocurrency, and they are setting their own terms and rules around its operations, in essence creating new self-sustainable mini-economies.

On September 7th, in partnership with Geektime, we held an hour long conference call with four cryptocurrency valuation thought leaders and over 400 participants. Each speaker shared their perspective on valuing crypto, for about 10 minutes each, and then the call was opened for questions from the audience. It was reiterated, time and again, that this talk was not meant for the purpose of investment advice, but as a discussion of valuation frameworks. A video replay of the call can be seen below. The slides from the call can be found on SlideShare (except Chris Burniske’s slides).

There are three types of cryptoassets: stores of value, security tokens, and utility tokens. General-purpose stores of value should be valued using the equation of exchange because these currencies are independent monetary bases. Examples include Bitcoin, Bitcoin Cash, Zcash, Dash, Monero, and Decred.

Although some may disagree, I also include the native tokens of smart contract platforms such as Ethereum, EOS, Dfinity, and Kadena in this category. Why? Because there’s a real chance that the native token of a smart contract platform that becomes sufficiently useful will emerge as an independent store of value.

One kind of token model that has become popular among many recent token sale projects is the “network medium of exchange token”. The general pitch for this kind of token goes as follows. We, the developers, build a network, and this network allows you to do new cool stuff. This network is a sharing-economy-style system: it consists purely of a set of sellers, that provide resources within some protocol, and buyers that purchase the services, where both buyers and sellers come from the community. But the purchase and sale of things within this network must be done with the new token that we’re selling, and this is why the token will have value.

If it were the developers themselves that were acting as the seller, then this would be a very reasonable and normal arrangement, very similar in nature to a Kickstarter-style product sale. The token actually would, in a meaningful economic sense, be backed by the services that are provided by the developers.

Crypto markets are very new with limited data history pertaining to crypto asset behavior, returns, and correlations. Many of today’s models are simplistic or limited, whether intrinsically (due to difficulty defining and measuring variables such as velocity and its counterparts, for instance) or extrinsically (due to limited applicability to different types of tokens, as seen with NVT and privacy coins, for instance).

In the future when the markets mature and asset relationships and behaviors are more discoverable, valuation models and ratios should be more predictive and informative. However, because of the very diverse nature of crypto assets, which can have different features, structures, payouts, etc., we may never have metrics and models as universal as the P/E ratio and DCF analysis for public equities.

In private equity, the J-curve refers to a portfolio’s cash flows, while in economics it is commonly used to describe the effects of currency devaluation on the national deficit. The basic idea of a crypto J-curve stems from how the market values a cryptoasset over time. The J-curve is a price manifestation of the above shifts in market sentiment and utility value. When expectations are initially high, so too is the price, but often largely composed of “discounted expected utility value (DEUV).” As expectations wane, so too does the price, even if “current utility value” (CUV) grows. Ultimately, as DEUV expands once again, the price of the asset should exceed its prior high as it is supported by more CUV.

Applying MV = PQ to crypto-currencies might yield numbers in the same order of magnitude as on-screen trading prices. But that’s down to a careful selection of the inputs or mere happenstance than any intellectual rigor. It is akin to observing two bananas, ten oranges and five apples on a table, and suggesting that (Apples = Oranges / Bananas). Mathematically correct but not scientifically sound.

I’m increasingly sceptical about the long-term value of utility tokens. When I wrote An (Institutional) Investor’s Take on Cryptoassets last year, I thought utility cryptoassets might end up being collectively worth hundreds of billions of USD, which is a lot of money but not enough potential return over current valuations to compensate for the risk. Now, I’m increasingly thinking that few or no utility cryptoassets will be long-term viable at all.

The velocity of tokens is a key aspect that affects future token value; however, it is also one of the least understood. This post attempts to describe velocity, how it impacts any token price over time and analyses the velocity of the Dala token as an example.

The equation of exchange is defined as: MV=PT

Where: M= money supply, V= velocity of money, P= average price level of goods, T= index of expenditures (such as the total number of economic transactions)

There is an emerging consensus that velocity is a confounding problem in establishing cryptoasset value for single utility tokens.

The widely accepted M = PQ / V model popularized by Chris Burniskeilluminates the effect of velocity on cryptoasset value quite obviously: increasing velocity decreases the value of the asset base in a linear fashion, and, therefore, the price of utility tokens (given a fixed token supply).

While there have been a lot of great discussions about why this model is effective for valuing this type of cryptoasset and quite a few valuations have been done based on it, we continue to ignore the massive white elephant that is the impact of velocity assumptions on these valuations. It is a variable that potentially throws all of these valuations out the door because we may very likely be underestimating velocity by several magnitudes if we just assume that people simply may not hold any of these tokens for any time.

Value capture is a topic that always seemed a bit overlooked in business modelling. Traditionally, as Peter Thiel frequently points out, there’s little correlation between value creation and value capture (e.g. one may generate tons of revenue without really profiting from it). Some industries have even established dynamics that clearly separate value creation from capture: think of the film industry, with production companies doing all the creative and operation work on one side, while, on the other hand, distributors and exhibitors take 80–90% of the share of profit, at the end of a movie’s life cycle.