INT

Going back to the core of Metcalfe’s law which states the network value is proportional to the square of users, how would this apply to an IoT network, where every user could potentially have multiple devices connected to the network? If the number of active users grow similar to a netoid function shape, which we see in Ethereum [Fig. 4] and NEO [Fig. 5], then this would have an effect on the steepness and height of the exponential portion of the curve before leveling off.

Fig. 4 Ethereum Address Growth (Etherscan.io)

Since no IoT network is developed enough with sufficient device participation to make a model of, we will have to make some assumptions around impacting variables and develop a model based upon them.

What do we know? INT is a Chinese project with a minimal amount of communication, lesser still to the western world, developing something that currently has no competing architecture for the given application, which has yet to release a main net and is not yet on any major US accessible exchanges.

This all takes me back to NEO, when people had to translate what little information was out there about a project that’s only (only) competition was Ethereum that fit a niche in China’s market.

Understanding that the cryptoeconomy is in much a different place than it was then, with many more investors involved and many ideals changed, the impact or growth of these variables may be different. Hype may have less impact, FOMO may not occur, or actual technical ability may have no impact on valuation and communication and hype may create all value, that much we cannot determine. So for this exercise, let’s assume the growth of this network is similar to that of NEO.

NEO’s network is currently 1/35th that of Ethereum at just over 1,000,000 addresses with an average daily activity of even less (1/100th of Ethereum’s daily activity) [Fig. 5].

Fig. 5 NEO address growth (neodepot.org)

Unfortunately, we don’t have address creation history for INT since it is still an ERC-20 token but we do know that there is currently ~80,000 addresses holding INT. Looking at NEO’s address creation chart, this corresponds to August 2017 when Antshares re-branded to NEO and when NEO’s main net had already been live for almost a year. That kind of highlights the difference between the cryptoeconomy of one year ago and now.

Based on this, I don’t think it is fair to compare where INT is now to where NEO was when it had this many addresses. Instead, lets use the percentage growth seen after Antshares re-brand as what may happen when INT releases main net and gets on a bigger exchange (4Q 2018). Looking at current unique address activity, averaging ~50–100 addresses a day, this pushes back the timeline to about March 2017. I feel like this is more fair to the timeline of INT’s development and what they have coming in the latter part of this year. INT will also not likely see the spike from a re-brand compounded with NEO’s 2017 run up but that growth may be seen on INT’s main net launch so we will match up the timeline there. I know we are really compounding our error bars here by making so many assumptions but what the heck.

In doing so we get an INT-NEO proportionality that we can apply to NEO’s active address history and model a potential INT network activity into the future. This gives us predicted network activity out ~1.5 years to March 2020.

In order to use this to find INT’s proposed market cap correlation to this predicted network activity, we have to first find the Metcalfe coefficient (the slope of the line) to NEO’s dataset and renormalize it to INT’s starting point market cap. This gives us a Metcalfe function similar to that of Ethereum of:

You can see that the slope of this line is MUCH higher than Ethereum. I believe this is because of the age of speculation that we are in that drives value without real world usage. This basically means that every user adds more value to the network than a user on Ethereum. Now this may be true for a data based network as in IoT, where the users are not just transacting value exchange but are also transacting data that may have value. Applying this formula to INT’s predicted dataset, we get: