Editor's note: Below is an excerpt from Tim Swanson's new book, "The Anatomy of a Money-like Informational Commodity," now available in PDF and at Amazon.



The charts in the chapter below are reused throughout the remaining sections of the book primarily because of their importance in probing the blockchain. Understanding how to read them is critical in discussing how to measure user adoption and growth.

For example, in July 2014, Pantera Capital, an investment fund focused on bitcoin, released their proprietary “BitIndex” which attempts to quantify bitcoin adoption without the use of monetary signals.[i]

Tim Swanson

In the chart above, the constituent parts are:

Developer interest on GitHub

Merchant adoption as a measure of consumer adoption

Wikipedia views measuring bitcoin education

Hashrate by logarithmic scale corresponding to orders of magnitude

Google searches captured by the number of times “bitcoin” appears

User adoption as measured by wallets

Transaction volume on the bitcoin network

The problem with the first 6-out-of-7 components in the Pantera non-monetary index is that they precisely measure the wrong thing, interest, and fail to accurately describe the right thing, adoption. Furthermore, in their announcement they mention that they intentionally underweight transaction volume; yet this is the only valid measurement in their entire index.[ii]

What does that mean?

In Naked Statistics, author Charles Wheelan describes a gift he received for Christmas one year: a laser golf range finder that he anticipated would improve his game due to its precision.[iii] Yet this did not occur because it did not measure what really needed to be measured: accuracy. In his words:

There were two problems. First, I used the stupid device for three months before I realized that it was set to meters rather than to yards; every seemingly precise calculation (147.2) was wrong. Second, I would sometimes inadvertently aim the laser beam at the trees behind the green, rather than at the flag marking the hole, so that my “perfect” shot would go exactly the distance it was supposed to go—right over the green into the forest. The lesson for me, which applies to all statistical analysis, is that even the most precise measurements or calculations should be checked against common sense.

People use Wikipedia to find out information that is complicated, when they do not understand it. Thus, there is a difference between what Pantera wants to measure (adoption and growth) versus what they did measure (interest, education and awareness).

Despite a creative effort, Pantera is not measuring what it is trying to measure. If a key indicator as to whether or not economies (virtual or physical) expand is based on Wikipedia viewership (like Bitcoin supposedly is), then Brazil’s GDP will most certainly see huge growth because the entry for Brazil was ranked 53rd in mid-July 2014.[iv] The reality is that Wikipedia views of Bitcoin represents interest or education and not usage or adoption – or in other words, interest grows but adoption does not.

In mid-July 2014, O.J. Simpson was the 779th most popular Wikipedia entry but readers (probably) were not trying to figure out how to lead an interstate highway chase during rush hour. Similarly, several Transformer movie entries rank in the top 350 but it is also unlikely that there was a simultaneous underground, organic movement for creating origami-like talking animatronics on wheels. Interest is not leading to adoption (conversion rates), why? As a friend explained to me, perhaps it is akin to opening lines at a movie: if they like it there will be lines, if they don’t then there won’t be. Maybe people, despite their awareness and exposure to Bitcoin just don’t like it or have no use for it.

The problem with the github commit component is that it is an input divorced from its output and suffers the same problem that U.S. News & World Reports(USNWR) statistics fail with in regards to ranking colleges. For instance, some of the metrics for USNWR deal with incoming GPAs, standardized tests and alumni donations which may correlate with a successful collegiate career for some students but do not necessarily cause it or lead to success afterwards. More on this later below.

What about on the output side of Bitcoin? How do we measure that with the protocol? One is if nodes and miners have upgraded their software.

Or in other words, at least two components of their index can be gamed, github commits and Wikipedia edits, and consequently the future of Bitcoin can get exponentially grander by merely spending a few dollars on Mechanical Turk.[v] For instance, Belkin, the electronic manufacturer was in the limelight 5 years ago for astroturfing: hiring people to write 5-star reviews of its products via the Mechanical Turk platform.[vi] Just as “click farms” have been created to inflate “Likes” on Facebook pages, there is no reason Mechanical Turk could not be used for making Wikipedia edits to juice that part of the index as well.[vii]

Likewise, the number of commits a github repo has, while on the face of it seems to measure developer activity, but it is unclear what the quality of these commits are or whether or not they are eventually removed. Or more importantly, whether or not the code is shipped and installed by miners and verification nodes. And this, the BitIndex does not measure: one-third of all nodes are still running version 0.8.x which are over a year old. Similarly, if a serious flaw and vulnerability was found in the core Bitcoin code base (bitcoind) which caused a cascade of hard forks that destroyed Bitcoin entirely, the github commit component would precisely measure the wrong thing, inputs, rather than an accurate attribute: healthy production code. In fact, that measure would spike, leading observers to believe that this collapse is good news for Bitcoin.

Charles Wheelan also describes this input versus output problem with USNWR which concurrently befalls the BitIndex. For example, why should alumni giving count for 5% instead of 1% or 10% in the USNWR? And what is the exact weighting for these corresponding seven components in the BitIndex? In his words:[viii]

For all the data collected by USNWR, it’s not obvious that the rankings measure what prospective students ought to care about: How much learning is going on at any given institution? Football fans may quibble about the composition of the passer index, but no one can deny that its component parts—completions, yardage, touchdowns, and interceptions—are an important part of a quarterback’s overall performance. That is not necessarily the case with the USNWR criteria, most of which focus on inputs (e.g., what kind of students are admitted, how much faculty are paid, the percentage of faculty who are full-time) rather than educational outputs. Two notable exceptions are the freshman retention rate and the graduation rate, but even those indicators do not measure learning. As Michael McPherson points out, “We don’t really learn anything from U.S. News about whether the education they got during those four years actually improved their talents or enriched their knowledge.”

All of this would still be a harmless exercise, but for the fact that it appears to encourage behavior that is not necessarily good for students or higher education. For example, one statistic used to calculate the rankings is financial resources per student; the problem is that there is no corresponding measure of how well that money is being spent. An institution that spends less money to better effect (and therefore can charge lower tuition) is punished in the ranking process. Colleges and universities also have an incentive to encourage large numbers of students to apply, including those with no realistic hope of getting in, because it makes the school appear more selective. This is a waste of resources for the schools soliciting bogus applications and for students who end up applying with no meaningful chance of being accepted.

Merchant adoption, as we have seen throughout this book, bears little correlation with consumer adoption in Bitcoin. In fact, merchant adoption tripled in the first half of 2014 without a similar increase in consumer usage. Hashrate similarly does not measure adoption, but rather, hashrate. Because of a steady decrease in distributed miners, the network is qualitatively less secure due to centralization and this is not measured or captured by hashrate. The hashrate is distributed not at the mean, but on one tail, leading to cartelization concerns.[ix] As noted by Jeff Garzik in chapter 1, for all intents and purposes the Bitcoin network is merely comprised of 12 people (mining pools) and at most 7,000 fully validating nodes and declining.

Google searches, as shown in chapter 9, has seen a continual decline since its absolute peak in December and correlates largely with the media boom-bust cycle. Perhaps this will pick up in the future, but this is not an accurate way to gauge adoption. Similarly, the increase in the number of installed wallets is not the same as the number of actively used wallets let alone user adoption.

For example, the amount of downloads of all Linux distributions is in the tens of millions but the amount of active users of desktop Linux is a small fraction of all operating systems by users (it varies between 1%-1.75%).[x] Download and installation does not mean usage. As noted later in chapter 8, the Bitcoin Android wallet has had a horizontal usage rate since February 2014 and because it is the most popular wallet it is possible that most other wallet providers have seen similar trends.

For example, the number of Blockchain.info “My Wallet” wallets steadily increase each month yet the corresponding “My Wallet Number of Transactions per day” and “My Wallet Transaction Volume” remains relatively flat the past six months: users probably forgot their wallet password and/or create a new wallet for each of their transactions.[xi] Or in other words, the number of “My Wallet Users” does not correlate with usage which likely means they are not new users. Contrary to Blockchain.info’s statements, they do not actually have 2 million users as they are conflating wallets with users.[xii] Similarly, as shown several times, the collective transaction volume on the Bitcoin network is flat and has been for 7 months despite significant merchant onboarding and increase in “wallet users.”

Brett King, author and founder of Moven, independently pointed out a similar phenomenon in July 2014:[xiii]

If we look at the most successful mobile payments initiatives in the US today, then the best candidates would be the Starbucks mobile app, Venmo and Dwolla P2P apps, and the mobile wallets of Google and ISIS. Bitcoin global transaction volume in USD peaked at US$180 million in June according to Blockchain.info, but the problem we’ve got is that it is unlikely that that transaction volume correlates with mobile wallet usage, in fact, we know it doesn’t. If it did we’d see wallet downloads improving transaction volume.

The more likely conclusion is again, these are not new users being added at Blockchain.info but instead are existing users creating new wallets because they misplaced their passwords or for features like Shared Coin (e.g., coin mixing).

How to measure the adoption and growth of bitcoin?

There are four charts that I show throughout this book that use data from the blockchain, the public ledger, which shows what is actually taking place on the network. Instead of guessing with laser range finders, I would argue that the four indicators taken together paint an accurate picture of adoption and usage: Number of Transactions Per Day (Transactional Volume), Bitcoin Days Destroyed, Miner Fees and Total Volume Output. Below is a description of each one.

Tim Swanson

The chart above is the on-chain transactional volume over the past year.[xiv] As noted throughout this study, in terms of visualizing consumer usage, this is arguably the most important chart. Despite a tripling or even quadrupling of merchant support, there has been very little corresponding on-chain growth. Instead, most of the growth is on the edges, in trust-me silos. The spikes in late November, early December 2013 correlate with the boom in market prices for bitcoins.

Tim Swanson

The chart above is the on-chain transactional volume over the past year.[xv] As noted throughout this study, in terms of visualizing consumer usage, this is arguably the most important chart. Despite a tripling or even quadrupling of merchant support, there has been very little corresponding on-chain growth. Instead, most of the growth is on the edges, in trust-me silos. The spikes in late November, early December 2013 correlate with the boom in market prices for bitcoins.

Tim Swanson

The chart above measures Total Transaction Fees (TTF), the total bitcoin value of transaction fees miners earn per day.[xvi] These are the seen fees and currently represent, at the time of this writing, between 0.2% and 0.4% of the total miners revenue which is more accurately captured in the Cost Per Transaction (via the block reward subsidy) discussed later in chapter 11.[xvii] Nonetheless, TTF is important because if more Bitcoin was attracting more on-chain users, they would collectively be paying more fees to miners for their transactions. As visualized above, TTF has been flat since November 2013 reinforcing the view that there has not been a large growth in on-chain usage (off-chain is not depicted as that information is proprietary).

Tim Swanson

The fourth chart (above) shows the Total Output Volume, the total value of all transaction outputs per day.[xviii] This is a good measure for visualizing the upper bound, the maximum amount of bitcoins that are sent in any given day. As seen here, the trends correlate with trading activity centered around the late November, early December 2013 boom. If bitcoin adoption and usage was increasing exponentially as some advocates claim, this chart would capture that.

Yet why doesn’t total output volume tell us the whole story of bitcoins used each day? Because it also includes “change” from return addresses which can throw off the real number by an order of magnitude upward; the real number is likely lower than shown above. The issue again is: is Charles Wheelan (or Pantera) measuring what was intended to be measured? Or is he using the laser range finder on the golf course, failing to see the forest for the trees?

For instance, during the early years of the Cold War a Soviet bomber was unveiled, Myasishchev M-4 Molot, that Western intelligence agencies were in retrospect, unable to quantitatively measure leading to false assumptions and policy faux pas – a “bomber gap.” This maskirovka (Russian, for military deception) was recounted in The Space Shuttle Decision:[xix]

Then, on May Day of 1954, at a public air show, the Soviets showed off a new jet bomber, the Bison. Here was another surprise-a Soviet jet bomber. It was all the more worrisome because no one in the U.S. had known of it until the Kremlin displayed it openly. A year later, in preparations for the next such air show, American observers saw a formation of 10 of these aircraft in flight. In mid-July came the real surprise. On Aviation Day, Colonel Charles Taylor, the U.S. air attaché in Moscow, counted no fewer than 28 Bisons as they flew past a review in two groups. This bomber now was obviously in mass production. The CIA promptly estimated that up to 800 Bisons would be in service by 1960.

In fact, Taylor had seen an elaborate hoax. The initial group of 10 Bisons had been real enough. They then had flown out of sight, joined eight more, and this combined formation had made the second flyby. Still, as classified estimates leaked to the press, Senator Stuart Symington, a former Air Force Secretary, demanded hearings and warned the nation of a "bomber gap." The flap forced Ike to build more B-52 bombers than he had planned, and to step up production of fighter aircraft in the bargain. Yet even when analysts discovered the Aviation Day hoax, they took little comfort. If Moscow was trying to fool the CIA, it might mean that the Soviets were putting their real effort into missiles rather than bombers.

This is similar to the illusion of a Potemkin village (Потёмкинские деревни) a fake village used to impress outside dignitaries. The most infamous was staged when Empress Catherine II of Russia visited Crimea in 1787. Her lover, Grigory Potemkin (namesake of the illusion) allegedly built fake villages with façades in the areas she travelled near; even going as far as to dress up as a villager all in an effort to fool Catherine by concealing the poverty of the area.

Measuring growth and in this case adoption and wealth is not just for history books but can also help market participants accurately view what is going on in a system like Bitcoin. The contemporary example corresponding to the Bison bombers would likely be subscribers and commenters at reddit Bitcoin, many of which are sock puppets and/or spam accounts used to game the karma system (i.e., systemically promote a scam or phishing website).

Tim Swanson

For instance, in the first week of July 2014, reddit subscriptions (pictured above) noticeably jumped by an order of magnitude.[xx] Was this new adopters rushing into the subreddit? Possibly, but probably not.

The last chart (below) for this section comes from Coinbase, a large consumer and merchant wallet provider.[xiii]

Tim Swanson

According to Brian Armstrong, co-founder of Coinbase, this also includes off-blockchain transactions (any under 0.25 bitcoins) between Coinbase users.[xiv] As of this writing, 0.25 bitcoins is roughly $150. What is noticeable is the same trend observed with the on-chain data, relatively flat transactional volume.

Or in other words, the reason the bitcoin price did not jump on news that Dell (the computer company) had partnered with Coinbase and accepted bitcoin payments in July 18, 2014 (as well as other supported merchants in previous months) is because very few people spend bitcoins in general and because there is no reason to use bitcoins to buy a Dell product when the same targeted consumer base already has credit cards. CheapAir.com, an online travel agency, did not fare much better, generating $1.5 million in bitcoin payments between November 2013 and July 2014.[xv] If the average flight is $300 roundtrip, this would amount to about 5,000 flights over the span of about 8 months.

It is unlikely that someone who has enough bitcoins to buy a computer or plane ticket doesn't already have a credit card that can do the same thing. Instead, bitcoin holders are going to use bitcoins for things they need which credit cards cannot be used for, not things that advocates on forums think the bitcoin holder needs. Again, there is a difference between the consumer behavior Bob wants to have versus what does happen. And at this time, based on observed actions, bitcoin holders do not necessarily need or want wares from Dell or CheapAir.com.

Bitcoin Market Opportunity Index

To be even handed, Pantera’s BitIndex is not the only inaccurate measure of growth and adoption. In August 2014, Garrick Hileman published an experimental Bitcoin Market Opportunity Index (BMOI) which attempts to rank countries that will most likely adopt bitcoin. Based on his metrics, Argentina is purportedly the most likely.

There are a number of fundamental flaws with his model, almost all of which involve the same problems that Pantera had:

Tim Swanson

Source: Garrick Hileman

Above is a table which describes the variables he used in determining the rank-order of countries.

Yet these metrics are not measuring actual adoption or usage of bitcoin. In actuality:

Global bitcoin nodes have dropped over the past year. In the past 60 days alone, the number has fallen from 7,672 to 7,089 nodes.[xvi][xvii]

Bitcoin client software downloads measures wallet inflation, not usage or adoption. Users cannot access the network without bitcoins.

The search term “Bitcoin” on Google, as shown later in chapter 8, has continually dropped since its peak in December 2013.

Bitcoin VC investment is not necessarily an accurate metric for measuring usage or adoption. As explored in chapter 13, Cleantech also attracted several billion in VC and angel funding. Yet it was unsustainable as most entrepreneurs were unable to build profitable business models and as a result, many went bankrupt.[xviii]

The full list of all 37 variables that Hileman uses is, as of this writing, inaccessible.[xxix]

Hileman’s methodology also includes set of variables, 39, including: technology penetration, remittances, inflation, black market, financial repression, bitcoin penetration and historical financial crisis. As of this writing, the full set of variables are unavailable.

Using a series of equations and weightings, he then produces the following table:

Tim Swanson

Source: Garrick Hileman

The table (above) is a list of countries that are, according to Hileman, the most likely to adopt bitcoin. Ignoring the economic issues discussed later in chapters 9 and 10, it is unclear how Zimbabwe, India, Nigeria or Nicaragua could adopt it from an infrastructure point of view.[xxx] It is also unclear why policy makers in the remaining countries would officially adopt it as well.

To compound matters, it is unclear what adoption actually means using this methodology. Does this mean that Argentinian central bank will begin buying bitcoins on the open market and then pay overseas bond holders with bitcoin?

In his words:

One of the first questions that arises in constructing a bitcoin adoption index is: what type of adoption should the BMOI measure?

For example, should the BMOI focus on where bitcoin is most likely to be used as a store of value? Or should it measure bitcoin’s commercial potential as a medium of exchange? And which of these two is more likely than the other to influence bitcoin’s geographic progression? The answers to such questions have a significant influence on the choice of index variables and weightings.

These are important questions and are thoroughly dissected later in chapter 9 and 10. The short answer to the second question is that bitcoins are a poor store of value due to their volatility – in the process of editing this book the market price of bitcoin fluctuated about 11% (between $564-$634).[xxxi]

Furthermore, it is unclear how Argentina’s policy makers could adopt bitcoin as-is today. The monetary stock (“market cap”) of bitcoin is about $7.7 billion. On August 1, Argentina defaulted on about $29 billion in debt.[xxxii] The logistics of how this transition could take place is not clear in Hilemann’s explanation and since Argentina's bondholders would likely want to instantly cash them in, trying would crash the market. This is further explored in the following chapter.

Mobile goal posts

As seen in throughout this chapter, a quandary for this space is that few people are actually looking at data that reflects the real health of the network. On the one hand there is a public, independent, transparent database called a blockchain that advocates are quick to point to as a disruptive technology because it is purportedly immutable. Yet when it comes to looking at behavior on this blockchain, very few people or organizations have discussed what is actually happening on it preferring to look at indicators that may be more favorable to their inclinations.

More often than not, such discussion devolves as the “goal posts” – the metrics considered as valid – are moved to some undefined point in time in the future in which these same measurements are then allowed to be valid. In the interim, the sole barometer and focus by many, seems to be price levels, which if John Kenneth Galbraith's works (discussed later in chapter 13) are any indication, could be a sign of unsustainable bubble activity.

Arguably the primary technological breakthrough is the blockchain and bitcoin (the currency or commodity or luxury good) is simply the first “appcoin;” one of many.[xxxiii] In fact, there are at least 83 other uses for it and multisig itself opens up a new world for managing digital and digitized assets.[xxxiv]

The next chapter discusses a couple potential uses-cases and where an ever increasing amount of bitcoins are born.