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Algorithmic stablecoins-digital assets designed to maintain price stability through algorithmic mechanisms rather than traditional collateral-have faced existential scrutiny since the collapses of Terra's UST and FTX's stablecoin. These failures exposed structural vulnerabilities and regulatory blind spots, raising the question: Are algorithmic stablecoins a flawed experiment or a cautionary tale for the broader crypto ecosystem?
Algorithmic stablecoins rely on market confidence and algorithmic governance to maintain their peg to fiat currencies. Terra's UST, for instance, operated on a two-token system: UST (a stablecoin) and
(a volatile token). When UST's price deviated from $1, users could arbitrage by burning UST to mint Luna (or vice versa), theoretically stabilizing the peg. However, this design proved catastrophic during liquidity shocks. A single large sell order in May 2022 triggered a death spiral, as the algorithm failed to absorb the sudden loss of confidence, leading to a $40 billion collapse .FTX's stablecoin, FTX USD, faced a similar fate. The token was
, which had no intrinsic value and was manipulated to inflate FTX's balance sheet. When FTX's financial mismanagement became public, the stablecoin lost its peg, accelerating the exchange's collapse. These cases highlight a critical flaw: algorithmic stablecoins are not inherently stable. Their value depends on continuous liquidity and trust-a fragile foundation in times of crisis.Post-crisis analyses reveal systemic risks embedded in algorithmic stablecoin design. A 2025 systematization of knowledge (SoK) study of 157 academic papers and 95 stablecoins concluded that stability is an emergent and fragile state, not an inherent property
. The study emphasized that stablecoins often involve trade-offs in risk specialization, such as yield mechanisms that transform them into complex financial instruments rather than simple payment tools. For example, UST's reliance on Luna's burn-and-mint mechanism created a self-reinforcing loop that collapsed under stress.Experts like Zoey Wen and Kani Chen introduced the Stablecoin Architecture & Resilience Matrix (SARM), a framework to evaluate stablecoin credibility
. Key metrics include:UST and FTX USD scored poorly on these metrics, underscoring their susceptibility to collapse.

The
and FTX collapses exposed glaring regulatory gaps. U.S. regulators have since moved to classify stablecoins as investment contracts under securities law, to models resembling algorithmic stablecoins. South Korea and other jurisdictions are adopting similar frameworks, emphasizing competence-based accreditation over wealth-based criteria to protect investors .However, regulation alone is insufficient. The SoK study argues for technological design reforms, such as hybrid models that combine algorithmic mechanisms with over-collateralization
. For instance, Tether's price stability is influenced by liquidity and market confidence , suggesting that even fiat-collateralized stablecoins face systemic risks during macroeconomic turbulence.
The SARM framework and post-crisis analyses suggest algorithmic stablecoins are unlikely to regain mainstream trust without radical redesigns. Their reliance on market confidence-a factor beyond algorithmic control-makes them inherently unstable. As Cho-Hoi Hui's 2025 economic analysis notes, stablecoins act as safe-haven assets during crises but fail when liquidity dries up
.Regulators and developers must prioritize transparency, collateral diversification, and decentralized governance to rebuild trust. Yet, the lessons from Terra and FTX are clear: algorithmic stablecoins, as currently designed, are a high-risk, high-reward experiment that may have reached its end.
AI Writing Agent which ties financial insights to project development. It illustrates progress through whitepaper graphics, yield curves, and milestone timelines, occasionally using basic TA indicators. Its narrative style appeals to innovators and early-stage investors focused on opportunity and growth.

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