The Memetic Theory of Bitcoin Demand
At CoinShares Research we’ve spent a lot of time thinking about how to value bitcoin. And while it, at least at the surface level, is a much more difficult task than most people may be used to, we are confident that we’ve found a framework that makes sense theoretically, and at the same time allows for some predictive analysis. One could call it the problem of valuing money that isn’t quite money yet, and this requires some creativity.
Our framework uses a Total Addressable Market (TAM) approach to value bitcoin as a form of money, iterating on our previous work. We define money broadly, arguing that ‘moneyness’ is a sliding scale and that any object can be used for the functions of money, but that only those objects with good monetary attributes will succeed over time. Taking a long-term view of bitcoin’s future, this framework allows us to value it based on some assumptions regarding its future penetration into the huge market use case of global money.
But while long-term analyses are both necessary and nice, the framework leaves something to be desired in its ability to shed light on shorter term developments. For many analysts, it’s simply not enough to have credibly derived long-term price targets—they also need the ability to analyse prices in the short- to medium-term. Our initial way to address this was through a time-based probability coefficient, which technically works, but we’re still left wanting better ongoing accuracy.
While we’ve previously laid out what we believe to be the fundamentals of short- to medium-term developments in bitcoin’s journey towards global ‘moneyness’, it has taken us some time to arrive at a proper methodological structure for measuring the progression of this journey, and contextualising it within a valuation framework. But at this point, we believe we’re getting there.
The Memetic Theory is Rooted in Biology and Anthropology
We’ve chosen to call our hypothesis of bitcoin demand (yes, we know we titled it ‘theory’ but that’s only because ‘The Hypothesis of Bitcoin Demand’ doesn’t sound nearly as cool) ‘memetic’ because we believe bitcoin demand is fundamentally driven by the spread of ideas. So when we use the word ‘meme’ we use it strictly in its anthropological sense as originally coined and popularised by Dawkins; that is, as an idea spreading within a population of hosts, much like a virus in biology, adapting and evolving on the way.
This means that there are a few different factors driving the spread of memes, some examples are:
- The general ‘virality’ or ‘infectiousness’ of the idea
- The inherent susceptibility or resistance to the idea in specific populations
- Outside or environmental pressures
Basically, the idea that bitcoin can be used as money spreads throughout a susceptible population from a set of current hosts. The newly infected hosts, if enamoured with the idea, will spread it further to a number of additional people (think back to the R-nought of COVID-modelling infamy), in an exponential manner.
At a population level, any specific population’s susceptibility to the idea of bitcoin as money is mainly a function of their lived experience, and can change over time as their lives take different turns. On top of this, favourable overall environmental pressures will also increase the rate of spread, and opposite.
All these factors come together to form a complex matrix of needs, beliefs, inherent and relational strengths of existing monies, monetary oppression, and so on.
Epidemiologically, the Meme Spreads Similarly to a Virus
The mechanism of spread is simple and similar to viruses. For the bitcoin meme to spread it must first be present in a host. The host must then spread the meme to a susceptible new host through some vector. Here, memes are at an enormous advantage to viruses in that they have the ability to spread very far and very fast. Whereas virus vectors are all physical and require physical proximity and movement, memes are information and may spread through remote information technologies such as writing, recording or telecommunications.
For a person to be susceptible to the bitcoin meme, they must have some level of need or at the very least an appreciation of the future need someone else might have for the utility bitcoin can provide through its monetary properties. So if a user has both a need for bitcoin at some level, and is in contact with a current host (or their writing or recordings), they may become infected and start to use bitcoin. If their experience with using bitcoin is then a positive one, they will likely act as hosts themselves, spreading the meme further.
Deepening the analogy, the bitcoin ‘patient zero’ was Satoshi Nakamoto, and the first susceptible population in contact with a host were the cypherpunks on the metzdowd mailing list. The cypherpunks were uniquely susceptible to the meme of bitcoin through their deep desire/need for private, non-state, censorship resistant money, and they were all in contact with the host through an email vector, allowing Nakamoto to spread the meme to the first set of new hosts through instant-message writing.
Source: CoinShares Research
From that group, the meme spread wider into new populations via the larger contact network of the group all the way to its current number of hosts. We’ve simulated this in the figure above and added a fictional R-nought of 3 for illustrative purposes.
Population-Wide Memetic Spread can be Visualised with Bell- and S-Curves
We can visualise meme spread through populations using a simplified visual language. If we sum up and abstract all humans on Earth into one single population, and assume a normally distributed susceptibility, constant virality and environmental pressures, and one catch-all use case of ‘money’, a distribution curve of meme spread might look something like this:
Source: CoinShares Research
Visualised differently, we get the familiar S-curve of adoption, which is the typical way in which new ideas or technologies spread throughout populations over time. At first, growth is relatively slow. Then the spread enters an exponential phase of rapid growth, before it slows down and eventually comes to saturation as the susceptible population is exhausted:
Source: CoinShares Research
Tying this back to our valuation framework, the basic idea is that as bitcoin usage increases throughout the total population, it gradually approaches the highest achievable level of the TAM valuation. In a total state of adoption, all people would use bitcoin for all monetary use cases. In such a scenario (regardless of likelihood), all else being equal, the total value of all bitcoins should be equivalent to the total value of all current money (or perhaps more since a single global money would have a greater network effect than any current money).
But Reality is More Complicated (and We can Use that to Our Advantage)
Back in the real world however, the abstraction of all global users of money and one all-containing use case of ‘money’ is not a very practical one. If we were to attempt any measurement of progress across such a broad population and such a breadth of monetary use cases all under one metric, we’d never get anywhere at all.
So out of this complexity, an opportunity arises: In order to ease the job of measurement, as far as practically possible, we can break out from the sum total curve above, a collection of narrower user/use case populations (we might also refer to them as groupings), each with their own individual TAMs and their own individual total users.
Source: CoinShares Research
When summed together, these groupings would add up to the total TAM of all monetary use cases and contain all monetary users (and for clarity, a single user can be a member of many use case groups, although the value of all the groups can only sum to the total value of all money).
Complexity Allows for Simpler Measurement
The benefit of splitting the global monetary user/use case group into many smaller subgroups is that it allows us to much more effectively narrow each group down for individual measurement. Summing them up is trivial.
Subgroups can be created for monetary use cases such as long-term savings, consumer payments, trading/speculation etc. These larger use case groups can in turn be broken into smaller more specific user subgroups like individuals, small enterprises and large enterprises, or by geography of the user/use case group. The breakdowns are somewhat arbitrary (but not entirely, as we’ll discuss two paragraphs down), and exist to make measurement easier, not necessarily to follow a specific set pattern.
For example, a specific subgroup could be UK-based small enterprise merchants using bitcoin to receive payments for goods or services, or US-based large enterprises using bitcoin as a long-term savings vehicle. Another example could be German individuals using bitcoin as a vehicle of speculation, whatever is most practical to group and measure.
Breaking things into smaller subgroups makes sense from a meme spread perspective as well. Viruses, for instance, don’t spread uniformly across the entire world at one time, they spread much more strongly within more tight-knit subpopulations where interactions are stronger and more frequent.
We believe that bitcoin spread thus far has acted similarly, spreading most virally through subgroups of similar users and use cases.
Incremental Progression Towards Global Monetary Usage is now Measurable
An approach like this makes it practical to measure bitcoin’s progression towards a theorised end state where it is used for all global monetary use cases and by all global users. Even if this end state is not likely to materialise, especially to its full extent, clearly defining the end state is very useful for calculating the highest achievable value of bitcoin, and then reducing your way down from there based on current adoption measurements and assumptions of future user/use case adoption.
Using this modelling approach would enable ‘live’ estimations of bitcoin’s value as far as it is justified by its adoption as global money. Not only then could we value it on a long-term basis, we could also get a sense of its valuation relative to price in the short- and medium-term.
Unfortunately, with regards to actually modelling this out, fleshing out the theory is the easy part (not that it’s been easy at all). The really hard part is still ahead of us. In order to make this model informative in any real sense, we still need to break down the user/use case populations in a way that makes sense, and then we need to actually find measurable data to feed it.
All of that work still lies ahead of us.
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