The relative value debate between $ETH and @solana extends to the #1 DEX's on each. Token valuation paints a baffling picture.
Fundamentals
- Raydium has seen tremendous growth over the last year, with daily volumes now at roughly 50% of that of Uniswap
- Raydium takes 25bps fees, 22bps of which goes to LPs and 3bps of which goes to buyback $RAY. By contrast, Uniswap's average fees are ~13bps, 100% of which goes to LPs and none of which goes to the $UNI holders today.
- The result is that Raydium now generates higher protocol fees than Uniswap, and $RAY token holders actually can accrue value.
- $RAY has seen much higher token inflation than $UNI but higher orders of magnitude top line growth meant revenue (that accrue to tokenholders) per token still grew 761% CAGR since November 2022.
Yet.. Valuations
- $RAY's MC is just over 10% of that of $UNI
- MC/protocol fees of $UNI at 6.7x vs. $RAY 1.4x, when @uniswap's protocol fees actually don't flow through to the holders just yet. And as mentioned before, Uniswap has a very complex ecosystem structure whereby equity holders of Uniswap Labs and the $UNI token holders compete for value accrual from the protocol.
- Raydium's MC/Revenues (which is actually more like P/E) now stands at <17x, which seems crazy low for a protocol that has been delivering a 3-digit % growth.
Why the mispricing?
Impossible to be definitive here, but possible explanations are:
- market is skeptical of the sustainability of meme/shitcoin/bot trading on Solana
- Uniswap has a well-regarded brand, team and investor base with contribution to thoughtful research.
- Liquid token investors aren't paying attention and markets are inefficient
- Investors are overly afraid of $RAY's token issuance (FDV/MC is 2.1x, though circulating supply has been steady since May 2024 with early investors/team unlock mostly all through).
What do you think?
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Lido allows users stake their ETH to secure Ethereum for yield without sacrificing the ability to do DeFi. $stETH is the liquid derivative token that's usable as collateral in other DeFi protocols such as @AaveAave and @MakerDAO
1/ Lido makes money by:
- Taking 10% of staking rewards (revenues)
- Paying half of that out to node operators (COGS)
- Paying staff and contributors (operating expense)
- Incentivising $stETH liquidity in other protocols (operating expense)
2/ Lido is quite close to being breakeven. As % of ETH staked rises and liquidity incentives eventually decline, Lido should be able to benefit from operating leverage.
Now that some time has passed since @blur_io's airdrop szn 1, I checked some high-level numbers to gauge whether it was good use of capital.
🧵
1/ Firstly, I was impressed that @PacmanBlur talked about the importance of capital allocation on @bankless ()
It's refreshing to know there are founders in crypto who are well-aware of this concept.
2/ As Mr. Buffett points out, capital allocation is a critical job that isn't easily mastered.
Company CEOs are unskilled in capital allocation, which "isn't surprising. Most bosses rise to the top because they've excelled in marketing.. or sometimes, institutional politics" 🏦
Arbitrum is essentially a spread business🖖. It takes in user gas fees as revenues and incurs L1 (Ethereum) gas fees and L2 compute fees as operating costs.
Protocol Earnings (called sequencer profits) are directed to the DAO Treasury. This has the impact of increasing Arbitrum's “book value” (i.e. Arbitrum has clear economic value accrual).
Future unknowns: how margins will evolve as the scaling roadmap for ETH plays out.
- EIP-4844/full Danksharding will reduce the cost of storing data for L2s by 10x/100x. To what extent will L1 batch fee cost savings be passed onto L2 users?
Firstly, law of large numbers is the phenomenon that the sample average converges to the population expected value as the number of trials increases.
The spraying will result in an averaging down of portfolio returns to the market average
🧵
1) In an industry where a winner-takes-all dynamic prevail (crypto is one because money and liquidity have big network effects), investors must go whale hunting rather than casting the net wide. Let’s illustrate with a simple example.
2) Consider the distribution of long-term crypto returns. It will look something like the power law distribution, NOT a bell-curve. Say for every 100 tokens, there will be 5 winners & 95 losers: