Why Have Some Algorithmic Stablecoins Failed In The Past?
Stablecoins are designed to trade at a steady value, usually one U.S. dollar, but not all of them use dollar reserves to hold that line. A subset of stablecoins tried to keep their peg algorithmically instead, and when markets turned volatile, some of those designs unraveled within days.
The short answer
Algorithmic stablecoins that have failed generally relied on a two-token system: one token meant to stay near a dollar, and a second, more volatile token that absorbed price swings by being minted or burned on demand. When large numbers of holders tried to exit the stable token at the same time, the mechanism meant to defend its peg instead flooded the market with the volatile token, crashing its price and breaking the very peg it was designed to protect.
How the minting-and-burning mechanism was meant to work
In a typical design, anyone could exchange one unit of the stable token for one dollar’s worth of the volatile token, and vice versa, at a fixed internal exchange rate. The idea was that if the stable token traded below its target price, traders would buy it cheaply, redeem it for the volatile token, and sell that for a profit, shrinking the stable token’s supply and pushing its price back up. If it traded above target, the reverse trade would expand supply and push the price back down. On paper, this created a self-correcting loop with no dollar reserve required.
Why the feedback loop could run in reverse
- A shrinking market absorbs new supply poorly. When redemptions accelerated, the system had to mint large quantities of the volatile token to honor them, and that token’s market had to absorb the new supply. If demand for the volatile token couldn’t keep pace, its price fell.
- A falling volatile-token price undermines the peg further. As the volatile token’s price dropped, each redemption produced even less real value, which pushed more holders to exit before further losses, creating a self-reinforcing spiral rather than a self-correcting one.
- Confidence, once lost, was hard to rebuild. Once holders doubted the mechanism would hold the peg, the incentive to redeem early outweighed the incentive to wait, and that expectation alone could accelerate a collapse regardless of the underlying math.
What separates algorithmic designs from reserve-backed ones
A reserve-backed stablecoin generally holds cash or cash-equivalent assets meant to match its outstanding supply one for one, so redemptions draw down a real reserve rather than a second market. That structure carries its own risks, including questions about what the reserve actually holds and how easily it can be converted back to a stable token under stress, but it doesn’t depend on a second token’s market price holding steady. Algorithmic designs removed that reserve requirement, which was part of their appeal, but it also removed the buffer that might have slowed a run.
The risks that remain relevant to any stablecoin
No cryptocurrency, including any stablecoin, carries the kind of FDIC or SIPC coverage that applies to a bank deposit or brokerage cash balance. A stablecoin’s peg is a design goal, not a guarantee, and history shows that goal can fail under enough pressure. Understanding what actually keeps a stablecoin’s price stable day to day is a useful step before treating any stablecoin as equivalent to holding cash. Regulatory treatment of these tokens is also still evolving, and rules that apply today may not reflect how a given stablecoin is classified in the future.
The takeaway
Algorithmic stablecoins that failed shared a common weakness: their peg depended on continuous market confidence and a healthy market for a second, volatile token, rather than on a dollar reserve sitting in the background. When that confidence broke, the same mechanism built to defend the peg became the channel through which it collapsed. Diversifying across asset types, rather than treating any single stablecoin as a cash substitute, is one way to think about limiting exposure to that kind of design risk.