The network effect of smart contract blockchains – a quantitative approach
Summary: Multiple cryptocurrencies are challenging Ethereum as the main smart contract blockchain. As the value of smart contract blockchains originates from its base of users and the applications utilizing the network, we take a quantitative approach to compare the network effect of Solana, Cardano, Avalanche, and Polkadot to Ethereum.
With a growing interest in decentralized applications, the popularity of the second-largest cryptocurrency, Ethereum, has surged over the past years. The surging interest has been followed by elevated scalability constraints and heightened transaction fees, leading to investors looking for an Ethereum-killer among other more scalable smart contract blockchains such as Solana, Cardano, Avalanche, and Polkadot. The value of these alternatives has increased considerably over the past years upon the anticipation that they can challenge Ethereum as the main smart contract blockchain.
As we see it, the value of smart contract blockchains originates from its base of users and applications utilizing the network. To express the value of a crypto network, Metcalfe's law can be applied. The law states that the value of a network is proportional to the square of the number of users in the system. For instance, the value of an Instagram account increases proportionally with the number of relatives also having an Instagram account.
However, one might argue that the network effect of smart contract blockchains is twofold, as the chicken or the egg paradox is present. For a smart contract blockchain to gain traction among users, the applications on the network need to be appealing. On the other hand, nobody wants to develop applications if there are no users on the network, creating the chicken or the egg paradox. This ultimately makes it challenging for newly developed blockchains to achieve a critical mass of users and applications to foster a network effect, whereas it is almost exclusively up to Instagram’s users to form its network value.
To express network effect, the great matter with cryptocurrencies is the fact that the public nature of blockchains makes the activity on the networks publicly available. This is truly interesting because the interpretation of the value of the network then becomes a public good. In this post, we take a quantitative view of the five largest smart contract blockchains being Ethereum, Solana, Cardano, Avalanche, and Polkadot, in which we compare five on-chain metrics divided by the market capitalization of the cryptocurrency:
- Price to sales
- Price to value locked
- Price to USDT and USDC supply
- Price per developer
- Price to NFT sales volume
Since the metrics are derived from market capitalization, it is most desirable to have as small a metric as possible. This means that the given number is high compared to the market capitalization. It is important to notice that this quantitative approach does not by default consider Layer 2 solutions on Ethereum nor Parachains on Polkadot, which are both beneficial for the networks.
For users to execute transactions on a network, they must pay a transaction fee. The size of transaction fees mainly depends on two elements, the demand for transactions and the total transactional output of the cryptocurrency. The fee is often a function of those two. Increased demand triggers enhanced fees, whereas increased transactional output lowers the fees. For instance, the severe scalability constraints of Ethereum are mainly to blame for its high transaction fees. However, users have so far been willing to pay these fees to benefit from its network, for example, to buy and sell non-fungible tokens from greater marketplaces, compared to the marketplaces on the other cryptocurrencies. As seen, Avalanche is presently having the lowest price to sales ratio, meaning Avalanche is generating the most in fees compared to market capitalization, however, it varies significantly.
In the majority of decentralized applications, users can lock value to either borrow or lend out crypto or provide liquidity to decentralized trading applications to receive compensation. Value locked is a good metric to compare between blockchains, since it illustrates the demand for such decentralized services broadly considering the network effect. For instance, when lending out, users do not only consider the interest rate but also the track record and technology of the application and the blockchain to study the features and risks. The higher value locked, the lower price to value locked ratio.
Stablecoins, which are 1-1-pegged cryptocurrencies to USD, are the backbone of smart contract blockchains and decentralized applications. The two largest stablecoins by far are Tether (USDT) and USD Coin (USDC) with a market capitalization of $82.9bn and $49.84bn, respectively. Their supply is, however, not solely issued on one cryptocurrency but many. With a higher supply of stablecoins, the cryptocurrency is used more for decentralized applications and as a medium of exchange, for instance, for remittance. Ethereum is home to the most USDT and USDC, while neither USDT nor USDC are issued on Cardano and Polkadot.
To create applications for the future, developers are required. According to an analysis by Electric Capital, Ethereum has the most developers in absolute numbers of 4,011 developers, although Polkadot has the most developers compared to its market capitalization of 1,400 developers. Solana, Cardano, and Avalanche have 890, 365, and 283 developers, respectively.
Non-fungible tokens (NFTs) have arrived at other smart contract blockchains other than Ethereum. However, Ethereum is still leading with its NFT ecosystem, however, Solana’s NFT ecosystem is drastically improving. Both Cardano and Polkadot do not have a notable market for NFTs.
The diminishing effect of Metcalfe's law
Taking the above metrics into account, it is clear that Ethereum surpasses the other cryptocurrencies on network effect in absolute numbers and some metrics with respect to market capitalization, ultimately giving rise to a superior network effect. However, Metcalfe’s law notes that the variable effect per user diminishes as a function of the size of the network. Given the twofold network effect, this entails that the first 1mn users and 1,000 applications bring about more value to the network than the next 1mn users and 1,000 applications. It is thus clear that the network of Avalanche and Solana have passed this point, at which the advantage of Ethereum’s greater network has proportionally decreased because Avalanche and Solana have gained the most troublesome but pivotal traction. On the other hand, it is evident that Cardano and Polkadot are still far from this point.
While the above approach gives an insight into the present network of a handful of cryptocurrencies, it is not a thorough analysis to naturally predict the future network effect, as the analysis barely touches upon the technological advancement of these cryptocurrencies. Since this whole lineup of cryptocurrencies has scalability constraints, one cryptocurrency cannot gain the whole market. This is ultimately the case of Ethereum, which has lost some of its first-mover advantage in the past years, due to its scalability constraints. While every cryptocurrency is intensely working on scaling its crypto, it is up to the future to decide which one quickest launch the most scalable but sufficient decentralized solution. It is fair to say that the one reaching this goal line first has much to win, if not all.
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