Bitcoin will likely evolve from the early go-go days to a more mature two-way market, driven by network growth and the pace of adoption, less by scarcity (which is written in stone and mostly baked in). So let’s do a deep-dive on historical adoption curves. 🧵
The chart above shows various S-curves going back to the early 1800s. Using the Historical Statistics of the United States database and the UN’s World Development Indicators, I found many interesting adoption curves in the US and around the world. /2
The chart shows growth of the US railway network during the early 1800s, cement & steel production during the industrial revolution of the late 1800s, the adoption of telephones around the turn of the century, and motor vehicle registrations during the early 1900s. /3
For the 20th Century, I found data for miles flown and the percentage of households with radios, TVs, and VCRs. And just to delve into the more-obscure side of things, I found the data for trolley miles in the 1920s, as well as measles immunizations. /4
More recently, we have global adoption curves for mobile phones, internet usage, and broadband subscriptions. And of course, I also show the growing network for Bitcoin and Ethereum. /5
While measuring very different things, these adoption curves show a similar pattern. And while most of these S-curves travel up and to the right, that’s not always the case, as some innovations become obsolete and get replaced by something else. /6
I had to create a common denominator to make the curves comparable. Miles flown and measles vaccinations do not use the same scale, after all. So I measured all these curves on a per capita basis (using either the US or global population, depending on the time series used). /7
The point is to show the shape of the curve on a log scale, rather than focusing on the starting or ending point. So, everything is shown as a percentage of population, on a log-scale. /8
What can these S-curves teach us about Bitcoin? Per Metcalfe’s Law, the larger a network gets, the more valuable it becomes. So, getting the curve right means getting the value right. /9
Continuing my last thought about Bitcoin and how its adoption curve compares to other "next big things" in history: This chart lines up curves for inventions such as cars and telephones and matches them to the start of the Bitcoin network in 2009. /10
Next, I normalize not only the starting point but also the starting value for all these curves. I do this by calculating a power regression curve for each of the independent values against the dependent value (Bitcoin), based on the shape of Bitcoin’s adoption curve. /11
Now we see that both BTC and ETH are following very closely in the footsteps of past adoption curves, whether they are old infrastructure curves like cement or steel production, or new digital infrastructure curves like internet and broadband. /12
While all these curves show exponential growth from here (even the trolley curve takes another decade before it declines), there’s a growing dispersion in the slope of these curves. /13
For the valuation of Bitcoin, this is a challenge. Even if we assume that the adoption curve will continue to grow (as is implied above), it’s difficult to calculate an expected return if you don’t know the slope of that curve. /14
Perhaps we can narrow the range of analogs to get a better picture of Bitcoin’s potential growth. The next chart highlights only the railroad, cement and steel curves, as well as internet, mobile phones and broadband. And trolley miles, just to play devil’s advocate. /15
The analogs now show a more unified set of outcomes, still up and to the right, but with fewer outliers. /16
It does appear that Bitcoin’s adoption curve might be flattening out a little earlier than the other curves (at least on a log scale), but perhaps I am reading too much into a yearly time series. /17
As for that trolley curve, in no way do I mean to suggest that Bitcoin will go the way of trolleys. But I am keeping the comparison in there as a sanity check. Not all S-curves come to fruition, and this is something we should keep in mind. /18
More on Bitcoin S-curves in the next thread. /END
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Will Bitcoin become a fixture? Here we see the number of global mobile phone users and global internet users, regressed against the number of BTC addresses. Both analogs support the idea that the Bitcoin (and Ethereum) network has a lot more growing to do. / 🧵
However, the different slopes for mobile phones vs internet users presents a challenge for valuing Bitcoin. How do you calculate a present value of future growth when it’s not clear how steep the slope is? /2
Based on a regression between price and network size, we can create an estimate for the fair value of Bitcoin down the road, using these two different curves as guides. "Higher than now” is suggested, but “how high” is also important, depending on one’s time horizon. /3
The sense of urgency about inflation is becoming palpable. Markets and the Fed seem to fear that inflation expectations will become unanchored enough that it will become difficult to put the genie back in the bottle. 🧵
Inflationary shocks usually begin as something tangible— supply chain bottlenecks or sanctions, for example. The risk is that it becomes psychological, whereby businesses and employees start demanding more for their products and services, just in case the inflation persists. /2
That mindset can feed on itself, creating more inflation in the process. The Fed knows this, which is why it needs to nip this inflation thing in the bud. But with the Fed powerless to resolve the supply side of the inflation problem, it has to focus instead on demand. /3
Bitcoin, boring? Gasp! But boring is good if you want institutional adoption. 🧵
Is the efficient market hypothesis replacing the go-go price discovery of yesteryear? The chart above shows Bitcoin’s fundamentals. The supply curve is dictated by the S2F model (h/t @100trillionUSD), and the demand curve is driven by network growth (Metcalfe’s Law). /2
Until recently, Bitcoin would often overshoot its intrinsic value to the upside during bull markets and to the downside during bear markets. It was a momentum game with little to no resistance, until the trend reached exhaustion. /3
Financial conditions are tightening again, which is what the Fed wants to see.🧵
Tightening the financial economy is the name of the game for the Fed right now. It aims to slow demand enough to slay the inflation dragon. /2
After a number of weeks of frustration, during which the Fed jawboned the market into tightening, only to watch financial conditions actually ease, the Fed's plan is back on track, as the chart shows. /3
What does gold know? With real rates rising as quickly as they are, the tried-and-true TIPS model suggests that gold should have fallen to 1600 or so. Instead, it is holding in like a champ at around $1950. Why? 🧵
Perhaps gold “knows” that if bond yields keep rising to the point of undermining growth, the Fed will be forced to dust off the 1940’s playbook by continuing/resuming its asset purchases indefinitely, much like the BoJ has been doing for years, and the ECB is hinting at now. /2
We are used to talking about a Fed put in the stock market, but what if in the future the put will apply to the bond market instead? If inflation becomes structural while the Fed financially represses bond yields, real rates could remain negative for a long time. /3
The stock market seems to be ignoring the Fed, perhaps even more so than first meets the eye. 🧵
If it was anticipating a prolonged, aggressive cycle by the Fed, the cost of capital that makes up the denominator in the discounted cash flow model (DCF) should be rising. But it's not happening so far. And depending on how the ERP is calculated, that disconnect is dramatic./2
A quick way to define the implied ERP is by subtracting the 10-year yield from the earnings yield, but that is not the most robust way to do it. A better method is to use the DCF model and to solve for what ERP is implied by the current S&P 500 index level. /3