An in-depth analysis of IntoTheBlock looked at the network effects of Bitcoin and Ethereum and found that it was much easier to calculate and track on Ethereum.
Ethereum’s role as a decentralized finance facilitator has the potential to amplify network effects and attract more users, while Bitcoin’s sparsely connected nature meant it had a much lower network effect value.
Looking at cryptocurrencies by network value
While cryptocurrencies seem to have been analyzed by all imaginable statistics, network effect is not often applied to digital assets.
The analysis applied Metcalfe’s law, which states that the value of a network is proportional to the square of the number of active users and is used to analyze modern technical networks such as Facebook and WeChat.
When applying to Bitcoin, Metcalfe’s law came to an interesting conclusion. According to a paper by the Swiss Finance Institute, instead of being proportional to the square of its users, Bitcoin’s network value seemed proportional to an exponent of 1.69 the number of users.
Bitcoin’s sparsely connected network is believed to have caused the lower exponent.
Relationship between the users of Ethereum and its value
Unlike Bitcoin, where only 1 million of the 30 million addresses with a balance are active, Ethereum has significantly more addresses.
The data from IntoTheBlock showed that there are a total of 38.57 million addresses with a balance on the Ethereum network. However, only 380,000 are active daily.
As with Bitcoin, the number of active daily addresses on Ethereum is closely related to the price of ether.
“This makes sense because users of ether can get more value from Ethereum if there are more users or applications using it,” explains Outumuro.
He also discovered that Ethereum’s role as infrastructure for DeFi has the potential to further enhance these network effects – as more developers work to improve the base layer of Ethereum, more dApps will be built on it, attracting more end users and in turn more protocol developers.
This, according to the analysis, creates a network effect very similar to what is commonly seen in large technology companies.
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