#BTC let me say something in general about models given there is an ongoing discussion with @100trillionUSD about which model does a better job in predicting BTC price. All models are wrong but some are useful.

What this means is that by design when you make a model you try to simplify an observed phenomenon with the simplest possible set of hypothesis.

S2F model is an interesting model because it is relatively simple and it uses something that has financial relevance (Stock to Flow) and is based on an apparent relationship (also a power law) between S2F and price. I was excited when I read @100trillionUSD original article because I observed power laws relationships in BTC since 2012 and this seemed a confirmation that BTC was behaving like a network (power laws are common in network-like phenomena). S2F also seemed to give some kind of mechanism of what causes this particular behavior.

But later I realized there were some problems. First of all S2F implies exponential growth over time. Initially it doesn't look like an exponential but it is well known that if you multiply something at a fixed rate (10x every 4 years in this case) you get an exponential over many cycles.

Also the apparent mechanism is not a mechanism at all given that you need to explain 1) why there is a 3.3 power between S2F and price 2) how S2F that stays constant between cycles can model the evident growth along a power line in time observed during bear markets.

S2F is a function of time anyway so observing a power relationship between S2F and price is actually observing a relationship between price and time.

To find a real cause we would need some function or fundamental principle that is not directly derived from the data, like Newton did with F=ma and F=Gm1m2/r^2 to explain why the planets have a power relationship between the size of the orbit around the sun and time it takes to go around it.

We don't have such simple theory of what drives the price of BTC. The Power Law in time is simply a heuristic model based on what we have observed so far.

It is more modest in its explanatory power but also more realistic. Anyway, when you study a new phenomenon like BTC scientists create often many models that try to explain the observed behavior.

Every model serves a purpose because it allows us to compare predictions and understand in more details the behavior of a system. It is ok if a model is falsified, in fact, a good model needs to be falsifiable that means it can be proven to be wrong when more data is accumulated. You never can prove that a model is perfectly true (it works for all possible cases and situations) but you can easily prove a model is wrong and this is how you do science.

You make progress in science by showing certain models of reality are not true or not true under certain circumstances (so they are just approximations of a deeper truth).

It doesn't matter who came up with a given model, each is a contribution towards understanding and it is not about ego in the end but truth and knowledge.

So even if there is some apparent rivalry between me and @100trillionUSD we are both fellow seekers in this search of the truth about BTC true nature.