Algorithmic Stablecoins: Lessons from Failures and Future Designs

Cryptocurrencies have disrupted traditional financial systems in numerous ways. However, one of the biggest challenges in the crypto world is creating a stable, reliable digital asset that can be used for everyday transactions. This is where stablecoins come into play.

While stablecoins like USDT (Tether) and USDC are pegged to the value of traditional fiat currencies and backed by reserves, there is a class of stablecoins that doesn’t rely on collateral but instead uses algorithms to maintain price stability. These are known as algorithmic stablecoins.

Though algorithmic stablecoins seem like an innovative solution to achieve price stability without relying on traditional assets, their design is not without flaws. The volatile nature of the cryptocurrency market, combined with poorly designed mechanisms, has led to spectacular failures in the past, causing millions (or even billions) of dollars in losses. But despite the setbacks, the quest to design a successful algorithmic stablecoin is far from over.

What Are Algorithmic Stablecoins?

At its core, an algorithmic stablecoin is a digital currency that aims to maintain a stable value, typically pegged to the US dollar, without relying on traditional reserves like fiat or crypto collateral. Instead, these coins use algorithms and smart contracts to adjust their supply based on demand, keeping their value relatively stable.

In simple terms, when the price of the stablecoin goes above $1, the system will issue more coins to increase the supply. If the price drops below $1, the system will reduce the supply of the coin to bring the price back to its peg.

Think of it as a self-regulating currency that attempts to function without relying on the conventional methods of backing like fiat or assets. This idea is appealing for its potential to create a fully decentralized, trustless, and scalable system. However, as history has shown, these systems can fail catastrophically under the wrong conditions.

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How Do Algorithmic Stablecoins Work?

There are several different types of algorithmic stablecoins, each with its mechanism for maintaining price stability. Below are the most commonly seen designs:

• Rebasing Coins

Rebasing stablecoins automatically adjust the amount in a user’s wallet. If the price of the coin is too high, the system “rebases” the supply by distributing more coins to holders, reducing the per-unit value. Conversely, if the price falls too low, the system will rebalance by removing some of the coins from circulation.

Example: Ampleforth (AMPL)

• Seigniorage-style Coins

These coins work by adjusting the supply of a secondary token to control the primary token’s price. When the price falls below the peg, users are incentivized to purchase bonds, which are essentially debt instruments. The bond issuance is then used to buy back tokens and restore the peg.
Example: Basis (a project that was shut down before it could launch)

• Dual-Token Systems

Dual-token systems involve the creation of two tokens: one acting as the stablecoin and the other acting as a form of collateral or a governance tool. When the stablecoin price fluctuates, users can burn or mint the second token in exchange for the first, thus controlling the supply.

Example: Terra (LUNA and UST)

While each of these models has its merits, none of them has truly proven to be resilient in a bear market or extreme volatility, as evidenced by the failures we will discuss later.

The Most Notable Failures

1. TerraUSD (UST) and Luna – A $60 Billion Collapse

One of the most famous and disastrous cases of an algorithmic stablecoin failure is TerraUSD (UST). This was an algorithmic stablecoin designed to maintain a 1:1 peg with the US dollar. The system relied on a dual-token model: UST (the stablecoin) and LUNA (the collateral token). When the price of UST fell below $1, the system encouraged users to burn UST in exchange for minting new LUNA. This arbitrage was meant to restore the peg to $1.

However, in May 2022, during a sharp market downturn, TerraUSD began to lose its peg. Investors started panicking and withdrew their UST in large quantities, increasing the supply of LUNA dramatically. The price of LUNA crashed from over $80 to mere cents, as the system failed to manage the growing supply and lost all confidence. The catastrophic crash wiped out $60 billion in market value.

What Went Wrong:

• Lack of Collateral: The algorithmic model was not supported by actual collateral (like US dollars or other assets), making it highly vulnerable when the market lost confidence in the peg.
• Market Sentiment and Panic: The system relied heavily on market participants believing in the stability of the coin. When confidence dropped, panic selling ensued, and the system couldn’t handle the liquidity pressure.
• Failure to Respond to Black Swan Events: The crypto market crash exposed the fragility of the system. When the market experienced sudden downward pressure, the algorithmic mechanics couldn’t react fast enough to prevent a downward spiral.

Lesson Learned:
While algorithmic stablecoins are ambitious, relying purely on code and market psychology without collateral is risky. These systems fail when trust evaporates, and no physical assets back the currency to absorb shocks.

2. Basis – The Promising Project That Was Never Launched

Basis was an ambitious algorithmic stablecoin project that aimed to implement a three-token model: Basis (the stablecoin), Bonds (debt instruments), and Shares (equity tokens). The bonds and shares acted as levers to control the coin’s supply and demand. If the price of Basis fell, users could buy bonds, and if it increased, they could buy shares.

Despite its innovative approach, Basis had to shut down in 2018 before it could launch due to regulatory concerns. The U.S. Securities and Exchange Commission (SEC) considered Basis’ bonds and shares to be securities, which could subject the project to heavy regulation.

What Went Wrong:

• Regulatory Challenges: The project faced hurdles from regulators, which ultimately forced it to shut down before even being launched.
• Complicated Mechanisms: The complexity of Basis' design made it hard to scale without requiring constant manual intervention and oversight.

Lesson Learned:
When creating a financial product that deals with user funds, it’s essential to consider regulatory frameworks. Projects must be flexible enough to adapt to changing laws and have a simple enough design to avoid overcomplicating the core mechanisms.

3. Empty Set Dollar (ESD) and Dynamic Set Dollar (DSD)

These two projects used rebase mechanisms to adjust supply and control prices. ESD and DSD attempted to maintain price stability through mechanisms that rewarded users for staking coins and penalized those who attempted to sell their coins when the price was below the peg. While the theory seemed sound, the projects struggled with the market’s cyclical nature.

What Went Wrong:

• Inability to Respond in Bear Markets: When the price of ESD and DSD fell below the peg during a bear market, users didn’t have enough incentive to buy more bonds or mint new coins. This led to a downward spiral that couldn’t be corrected.
• Unsustainable Incentives: The rewards offered to users didn’t last long enough to keep people interested in the system during down markets. There were no real-world use cases driving demand for the token, which ultimately made the system rely on speculative behavior.

Lesson Learned:
Incentive structures need to be built to withstand prolonged bear markets. If the system relies too heavily on speculative demand, it won’t have the staying power needed to maintain stability.

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Why Algorithmic Stablecoins Fail

Algorithmic stablecoins promise decentralization, transparency, and scalability, but they often fail due to several core issues:

1. Overreliance on Market Psychology
The fundamental flaw in many algorithmic stablecoin systems is the reliance on market sentiment. If confidence is high, the system might work as intended. However, if panic sets in, due to an external shock or loss of confidence, the entire system can collapse. The assumption that market participants will always act rationally is a dangerous one.

2. Lack of Collateral Backing
Most algorithmic stablecoins have no physical assets backing them. In contrast to fiat-backed stablecoins, like USDC, that can be redeemed for real dollars, algorithmic stablecoins depend entirely on the trust placed in the system’s algorithm. When this trust is eroded, there’s nothing to cushion the collapse.

3. Vulnerability to Market Crashes
Algorithmic stablecoins are vulnerable to large-scale market downturns. During these periods, the demand for tokens often dries up, and no algorithm can prevent the subsequent cascading failures. Without a mechanism to provide liquidity in times of crisis, these coins cannot hold their peg.

What Can We Learn from Failures?

The failures of algorithmic stablecoins offer key insights that can inform future projects:

• Incorporate Collateralization: Even partial collateralization can provide a buffer against market shocks.
• Create Resilient Incentive Structures: Incentives must work in both bull and bear markets, ensuring stability even when speculative behavior dries up.
• Focus on Transparency and Governance: Clear communication about the mechanics of the system and transparent governance can build trust and reduce panic during stressful periods.
• Plan for Black Swan Events: Algorithmic stablecoins must have contingency plans and fail-safe mechanisms to ensure they can withstand extreme market conditions.

The Future of Algorithmic Stablecoins

Despite the failures, there’s still hope for algorithmic stablecoins. The future may involve more hybrid models that combine collateral with algorithmic systems. For example, Frax Finance has introduced a partially-collateralized model, where a fraction of the stablecoin is backed by collateral (like USDC), while the rest is algorithmically controlled.

Another promising development is Ethena's USDe, which employs complex hedging strategies to create a stable digital currency.

The key takeaway is that while algorithmic stablecoins have faced setbacks, they aren’t entirely out of the game. By learning from past mistakes, future designs may be able to overcome the flaws that plagued previous attempts.

Final Thoughts

Algorithmic stablecoins represent one of the boldest experiments in the financial world. While they offer the potential for a fully decentralized, trustless, and scalable digital currency, they also come with significant risks. The lessons from past failures should guide future designs, making them more resilient, transparent, and capable of withstanding market turbulence.

As the crypto space continues to evolve, the quest for a perfect algorithmic stablecoin might be closer than we think. But only with careful thought, robust incentive structures, and adaptability to market conditions will these systems be able to hold their ground and succeed.