Algorithmic Stablecoins

What is the core mechanism of algorithmic stablecoins and how do they attempt stabilization?

Algorithmic stablecoins operate without traditional collateral, relying instead on automated smart contracts to manage supply and demand dynamics, often referencing the Quantity Theory of Money (QTM: M × V = P × Q). If the stablecoin's price (P) drops below $1, the algorithm reduces the money supply (M) by incentivizing users to burn the stablecoin in exchange for a governance token or bond-like instrument. If the price rises above $1, the algorithm increases the supply by minting new tokens. The goal is to use these automated expansions and contractions to perpetually force the price back to the $1 peg. This mechanism relies heavily on speculative trust and the willingness of arbitrageurs to participate in the burning and minting processes.

Why have algorithmic stablecoins historically failed, leading to 'death spirals'?

Algorithmic stablecoins are vulnerable to 'death spirals' because their stabilization mechanism collapses under sustained selling pressure. When the peg breaks (P < $1), the system attempts to contract the supply by issuing governance tokens to absorb the excess stablecoins. However, if market confidence is lost, the governance tokens themselves lose value rapidly. This creates a vicious cycle: the stablecoin drops, more governance tokens are issued, their value plummets further, and the market loses faith in the system's ability to recapitalize itself. The most notable failure, Terra/LUNA in 2022, demonstrated that without tangible, liquid collateral, the algorithmic mechanism cannot withstand a massive, coordinated speculative attack or market panic.

What are seigniorage shares and how were they used in failed algorithmic models like Basis?

Seigniorage shares were a mechanism used by early algorithmic stablecoins, such as Basis (2017-2018), to manage supply. When the stablecoin traded below $1, the protocol would sell 'shares' or 'bonds' at a discount to arbitrageurs, raising capital and reducing the circulating supply of the stablecoin. The idea was that these shares would be redeemed for a profit when the stablecoin returned to $1. However, this model failed because it relied on the assumption of future demand and stability. If the market doubted the stablecoin would ever return to $1, no one would buy the discounted shares, leaving the protocol unable to contract the supply, leading to a permanent failure of the peg and the collapse of the system due to a circular incentive failure.

What is the primary problem with algorithmic stablecoins regarding purchasing power and stability?

The primary problem is their inherent instability and vulnerability to speculative attacks, resulting in unstable purchasing power. Unlike fiat-backed stablecoins, which offer guaranteed $1 redemption backed by liquid assets, algorithmic models offer only a promise of future stability based on complex incentives. During periods of market stress, this promise evaporates, and the stablecoin's value can rapidly decouple from $1, often dropping to near zero. This makes them unsuitable for use as reliable collateral, medium of exchange, or store of value, as demonstrated by the failure of multiple high-profile projects. No purely algorithmic stablecoin model has yet proven successful in maintaining a robust peg through multiple market cycles.