With the recent proliferation of alternative layer-1 (standalone) blockchains and layer-2 scaling solutions, the marketplace for Us and L2s has become significantly more saturated. There are currently around 140 smart contract- enabled blockchains and rollups being monitored by DeFiLlama's activity tracking, although Ethereum accounts for over 57% of the total value locked (TVL) sitting on these networks. (That's US$24.3B out of a comprehensive TVL of $42.2B as of early December 2022.) That share is unchanged from the end of fast year, albeit down from Ethereum's almost 70% dominance in mid-2021, when the L1 race took off in earnest.
Other Us and L2s among the top 10 platforms account for the next 34% of TVL in the crypto ecosystem. Amid the top Us/L2s, the names haven't changed much in the last year except for the notable loss of Terra-Luna and the inclusion of Optimism (see charts 17 and 18). But the ordering is now different. For example, TVL data suggests that activity has been drifting away from alt Us Like Avalanche and Sofana in favor of Tron and Ethereum-based scaling solutions like Polygon, Arbitrum, and Optimism. Indeed, Tron was able to increase its proportion of TVL activity from 2% to 10% of the crypto universe total.
Chart 17. TVL by L1/L2 (31 Dec 2021)
Sources: DeFiLlama and Coinbase.
Chart 18. TVL by L1/L2 (30 Nov 2022)
Sources: DeFiLlama and Coinbase.
Separately, BNB Smart Chain has been able to maintain its share of the overall market (13%), second only to Ethereum. Notably, the largest DeFi protocols on Tron and BNB Smart Chain are both forks of Ethereum-based protocols,Compound and Uniswap, respectively. One way to look at the corresponding tokens backing these networks is to estimate the expected market capitalization as a percentage of ETH‘s market cap. That can provide a better sense of how well the value corresponds to the relative level of development on these chains. From that perspective, BN B (BN B Smart Chain) has been the clear winner increasing its valuation in 2022 to around 30.2% (from 19.6% at the end of 2021), followed by MATIC (Polygon) at 5.2% (up from 3.5% at the end of last year). Meanwhile, the market caps of previous “blue-chip” names like AVAX (Avalanche) and SOL (Solana) have come down from their peaks reached in 2Q22 and 4Q21 respectively to 2.7% and 3.3% (of ETH's market cap).
Chart 19. Market cap of select tokens as percentage of ETH
Sources: TradingView and Coinbase.
Something peculiar about the activity patterns on these Us and L2s, however, is that their respective network traffic does not necessarily follow the cyclical up and down trends in the crypto markets, as one might expect. The change in quarterly transaction growth varies by network rather than moving in tandem with other chains, which may hint at the broader competition among Us and L2s for an ostensibly finite pool of users and capital. In our view, this proves that there is healthy user demand for solutions that address issues of scalability, speed, and/or transaction fees, but what is less obvious is whether this will become a winner-take-all market.
Chart 20. Quarterly transaction growth by chain
Sources: Etherscan, BcsScan, TRONScan, PolygonScan, ArbiScan, Optimistic Etherscan and Coinbase.
From Fat Protocol to Fat Application
Most if not all protocols start out following the Fat Protocol thesis, which proposes that value tends accrue to the protocol layer over the application layer. This approach is different from the web2 internet model where investing in companies like Google, Amazon, and Meta (formerly Facebook) has historically produced higher returns than investing in web infrastructure technologies. This was the case for Ethereum back in 2016, when the theory was first proposed.
But over time, as user adoption grows and developers build more applications on these networks, mature protocols seem to be expressing the Fat Application thesis. That is, the value of all the things built on top of a blockchain must eventually surpass the value of the underlying blockchain itself. Moreover, the ardent competition among blockchains has pushed fees lower and lower to capture a greater share of network activity, while application users have often continued to pay for specific services, depending on factors like product/market fit. (Incidentally, this reduces the utility of metrics Like TVL in our view because it fails to capture important factors like revenues and cash flows.) The investment thesis should thus focus on whether the growth rate of dapps on a given network is higher than the growth rate of the network itself and judge the investment opportunity accordingly.