Understanding automatic market makers
The real strength of tailored AMMs might lie in their capacity to cultivate deeper loyalty and engagement within niche communities
Jack_the_sparow and Adobe modified by Blockworks
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Automated market makers (AMMs) offer continuous market liquidity and decentralized trading mechanisms that make a whole host of DeFi innovations viable. Recently, a few projects — including memecoin launchpad pump.fun — have taken an interest in developing their own use case-specific AMMs, veering away from reliance on third-party platforms.
To understand this trend, we need a more intimate understanding of how AMMs work, the problems they solve, and the strategic advantage of customizing liquidity mechanisms to align with project objectives.
AMMs remove TradFi’s order book model entirely, instead replacing the concept with liquidity pools. These are user-supplied reserves of tokens (typically equal in value within constant-product concepts) that get locked inside of smart contracts. Instead of matching buy and sell orders directly, AMMs facilitate trades using formulas that continuously determine token prices based on their relative quantities within each pool.
One of the most prevalent AMM models utilizes the constant-product market maker algorithm, expressed mathematically as x * y = k. In this markup, x and y represent the reserve balances of two tokens in the liquidity pool, while k is a constant representing the product of these reserves.
When a user conducts a trade, exchanging token x for token y (or vice versa), the quantities shift, altering the ratio of tokens in the pool. To maintain the invariant constant (k), the AMM algorithm adjusts the price dynamically, ensuring the product of the token quantities remains stable post-trade.
If it’s helpful, you can also just pretend this all happens by magic, the mechanics of which are entirely indiscernible to all but the most learned pursuers of the occult. The outcome is the same either way, with the key point being: At the end of a trade, a given liquidity pool will always remain balanced according to the AMM’s ruleset. Through the maintenance of this balance, the system preserves a continuous state of liquidity and predictable token pricing without reliance on centralized counterparties. Abracadabra.
Of course, this model also introduces certain trade-offs. Impermanent loss for one, which can occur due to price fluctuations and alter token ratios. AMMs also typically require significant liquidity to reduce slippage (the difference between expected and executed trade prices). When a liquidity pool has limited reserves, even relatively small trades can substantially impact the token ratio, causing notable price swings during execution.
So why would a project that is not primarily concerned with generalized token swapping want to operate its own AMM? Let’s take pump.fun as a recent for-instance. It initially employed a bonding curve mechanism for token launches, where token prices increased as more tokens were purchased. Once a token’s bonding curve was completed, it would migrate to third-party provider Raydium to facilitate all of the aforementioned AMM balderdash.
However, pump.fun has recently transitioned to its own proprietary DEX, PumpSwap, eliminating the need for migration to Raydium and the associated fees. By internalizing its AMM, pump.fun gains complete control over liquidity dynamics, and can customize incentives and trading features to match the unique speculative and meme-centric interests of its community. For users, this means a more streamlined trading experience with scaled back costs.
As Web3 platforms progress, it’s clear that some believe tailored AMMs offer competitive advantages and facilitate closer alignment of economic incentives with community-specific behaviors and objectives. Thus, the real strength of tailored AMMs might lie not just in their financial or technical innovations, but in their capacity to cultivate deeper loyalty and sustainable engagement within niche communities.
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