The algorithmic stablecoin UST was once a shining star in the cryptocurrency space, aiming to maintain its peg to the US dollar through algorithmic mechanisms rather than traditional collateral. What set UST apart from other stablecoins (like USDT/USDC) was its attempt to control cyclical feedback through free market dynamics of individual behavior, without relying on centralized institutions and real-world asset (RWA) input. However, the collapse of UST sounded the alarm for the entire crypto industry and provided valuable lessons for reassessing the design of algorithmic stablecoins.
The core of UST’s stabilization mechanism was LUNA. When UST’s price exceeded $1, users could burn LUNA to mint new UST, thereby increasing the supply of UST and lowering its price. Conversely, when UST’s price fell below $1, users could burn UST to mint LUNA, reducing the supply of UST and raising its price. This mechanism was intended to create an arbitrage opportunity, encouraging traders to keep UST’s price around $1.
However, UST’s fatal flaw lay in the fragility of its value foundation. LUNA lacked intrinsic value and its price was highly dependent on demand for UST. When market panic set in, LUNA’s price collapsed rapidly, causing UST to lose its peg and ultimately crash. The collapse of UST highlighted the potential risks of algorithmic stablecoins and raised doubts about their stability.
Despite UST’s eventual failure, some ideas embedded in its mechanism are still worth in-depth exploration. UST attempted to establish a decentralized feedback loop that maintained price stability through the free interplay of individual behavior. This feedback-loop-based mechanism, in theory, possesses high resistance to censorship and a decentralized nature. In fact, UST’s mechanism was a complex human-machine interactive system, where individual trading behavior influenced the algorithm’s operation, and the algorithm in turn affected individual decisions. This model bears resemblance to Satoshi Nakamoto’s vision of enabling the Bitcoin network to perceive real-world prices in a decentralized manner.
While he was alive, Satoshi was constantly trying to solve the problem of allowing the Bitcoin network to perceive BTC prices or other real-world values, which was essential for resolving the core pricing issue of BTC payments. The state channels proposed during Satoshi’s era already solved the efficiency issue of BTC payments. However, the fatal problem of the algorithmic stablecoin UST was its value base being the “air coin” Luna. Still, the human-machine interactive value feedback loop between Luna and UST—just like the Bitcoin network—is a decentralized, distributed, human-machine adaptive nonlinear complex system. The key point of perception is that it incorporates the process of distributed human-machine interaction based on individual behavior.
So how can we design a more robust algorithmic stablecoin? One key question is how to design a more solid value foundation for algorithmic stablecoins and how to leverage the Bitcoin network and the behavior model of distributed individuals.
First, we need to solve the “air coin” problem of LUNA. Instead of relying on a token with no intrinsic value, we could consider introducing assets with intrinsic value as a value foundation. These could be mainstream cryptocurrencies such as Bitcoin and Ethereum, or tokenized real-world assets (RWA) such as gold, stocks, or real estate. In addition, we can explore distributed value discovery mechanisms, using activities on the Bitcoin network (such as transaction volume, miner rewards, smart contract interactions) to dynamically evaluate and adjust the value base. A possible solution is to adopt a hybrid model that combines partly intrinsic-value assets with value assessment based on network activity.
Second, we can consider directly connecting BTC and UST to achieve distributed BTC/UST interaction. This may involve designing a protocol that allows users to directly mint and redeem UST using BTC, without relying on an intermediate token. This model could simplify the mechanism and improve efficiency, but also requires solving several key challenges. To enable direct BTC/UST interaction, a reliable oracle network is needed to provide accurate and timely BTC price data. This oracle network must be highly decentralized to prevent price manipulation. Additionally, a complex algorithm must be designed to dynamically adjust UST’s minting and redemption mechanisms based on BTC price fluctuations and UST supply and demand. This algorithm must be capable of withstanding market volatility and preventing UST from losing its peg.
When designing algorithmic stablecoins, we also need to consider the following key factors:
The stablecoin must be capable of handling a high volume of transactions and support future growth. This may require the use of efficient consensus mechanisms and scaling technologies.
In conclusion, designing a successful algorithmic stablecoin is an extremely challenging task. UST’s failure has provided valuable lessons for the development of stablecoins. While pursuing decentralization and innovative mechanisms, we must always keep in mind the importance of value foundation and risk control. Leveraging the Bitcoin network and distributed individual behavior models offers some promising ideas for solving the value base problem. However, realizing these ideas will require overcoming many technical and economic challenges, along with extensive research and development.
