What Is an Algorithmic Stablecoin and How Does It Work? Risks and Examples for Indian Users

Crypto provides an alternative way to move and hold value outside traditional banking systems. But these assets are also known for large price swings. This makes them less practical for everyday spending.

Stablecoins solve this problem by combining the benefits of crypto with the stable value of assets like the US dollar.

Most stablecoins back their value with real-world assets. But algorithmic stablecoins take a different route. Instead of holding reserves, they use code to control supply and keep prices stable.

That said, it doesn't come without risks either.

In this article, we'll look at how algorithmic stablecoins work, where they have failed before, and what you should know before using them.

• Algorithmic stablecoins use code and market incentives to hold a $1 value, with no real-world reserves backing them.
• There are three main types: rebasing, seigniorage, and fractional-algorithmic stablecoins.
• The collapse of TerraUSD wiped out an estimated $40–50 billion and later led to a 15-year prison sentence for founder Do Kwon.
• Purely algorithmic stablecoins are largely unrecognized under current regulations in the US and EU.
• For Indian businesses, international payments still move through regulated banking channels, not crypto-based networks.

An algorithmic stablecoin is a digital asset created to maintain a stable price, usually around the US dollar. It works without backing itself with cash or other real-world reserves. Instead, it has a set of automated rules built into smart contracts that manage the token's supply to keep its price in check.

If too much demand is pushing the price up, more tokens are issued. On the other hand, if the price drops below the peg, supply gets reduced to bring it back.

Other stablecoins don't work this way.

Fiat-backed stablecoins like USDC hold actual dollars in a bank account. They are also audited and regulated. This makes them reliable, but you're trusting a centralized issuer to manage those reserves honestly.

Stablecoins such as DAI are backed by cryptocurrency instead of algorithms alone. To create them, users must deposit crypto collateral that is typically worth more than the stablecoins being issued. This removes the need for a central authority but ties up a lot of capital.

Algorithmic stablecoins skip both approaches. They run purely on market incentives and automated rules.

Different approaches have been used to build stablecoins that do not depend on full reserve backing. The three main types are:

Rebasing

Stablecoins such as Ampleforth use a rebasing mechanism that changes token balances automatically. When the price moves above $1, more tokens are added to users’ wallets. If it falls below, balances shrink.

Your share of the total supply does not change, but the number of tokens in your wallet does. The goal is to push the token price back toward $1 even as supply increases or decreases.

Seigniorage or Dual-Token

These models use two tokens to manage stability. One is the stablecoin itself. The other is a bond or share token that absorbs price swings.

Basis Cash used three tokens: BAC (the stablecoin), Basis Shares, and Basis Bonds. When the BAC dropped below $1, users could buy Basis Bonds at a discount and redeem them at $1 once the peg recovered. The BAC used to buy those bonds was removed from circulation, reducing supply and pushing the price back up. When BAC traded above $1, new tokens were minted and distributed to Basis shareholders.

Fractional-Algorithmic

This is a hybrid model. Instead of going fully algorithmic, it backs part of the stablecoin with real collateral like USDC and manages the rest through an algorithm.

In a model where a stablecoin is 85% collateral-backed and 15% algorithmic, reserves provide most of the stability while automated systems manage the rest. Frax became the most popular example of this approach.

But after the Terra collapse, the Frax Finance community moved to full collateralization, dropping the algorithmic component entirely.

Minting adds new tokens into circulation. Burning takes them out permanently.

The protocol increases token supply when the price moves above $1, which helps lower the price. When the price falls below $1, it burns tokens to reduce supply and move the price upward again.

No collateral changes hands in any of this. The whole thing runs on code and on people trusting that the mechanism will hold. Once confidence disappears, the peg can fail.

TerraUSD (UST) was an algorithmic stablecoin built by Terraform Labs, founded by Do Kwon. It was designed to hold a $1 peg through a link with a sister token called LUNA. Users could burn $1 worth of LUNA to mint one TerraUSD, or exchange one UST for $1 worth of LUNA. Arbitrage traders were supposed to keep the peg in balance.

At its highest point in early 2022, LUNA was valued at close to $120. Around the same period, Anchor Protocol gained popularity by offering roughly 20% annual returns to UST depositors.

Then, in May 2022, large amounts of UST were sold off suddenly. As UST fell, more LUNA had to be minted to try to restore the peg. That flooded the market with LUNA, crashing its price. A falling LUNA meant UST had even less backing, which caused more panic selling.

The Luna Foundation Guard had built up $3.5 billion in Bitcoin to defend the peg and deployed it all. The collapse continued anyway. Within days, both tokens were almost worthless, destroying roughly $40–50 billion in value.

MIT Sloan research shows that the collapse wasn't a coordinated attack. It started when a few large UST holders quietly exited on May 7. Others could see this happening on-chain and followed.

It was also revealed that Kwon had secretly paid a trading firm to prop up UST's peg during a smaller crisis in May 2021. He then publicly claimed the algorithm had fixed it on its own. He was sentenced to 15 years in prison in December 2025 for fraud.

What this teaches us about algorithmic stablecoin design?

UST showed that an algorithmic stablecoin runs on confidence. As confidence disappeared, the entire system began to break apart. There was no real collateral to stop the fall.

It also showed the risk of building demand on high yields rather than real use. When the 20% returns on Anchor Protocol looked unsustainable, users pulled out fast, and the peg never recovered.

Depegging happens when a stablecoin no longer holds its intended $1 value. In algorithmic stablecoins, even a small drop can spiral into a major crisis.

When confidence drops, people sell. That selling pushes the price down further, which causes more people to sell. The mint and burn mechanism, which is supposed to fix the imbalance, can break down under this kind of pressure.

As more sister tokens enter the market, their value drops and the stablecoin loses the support behind it. This chain reaction is known as a death spiral. Once it begins, algorithms alone often cannot stop it.

It's similar to a bank run. When too many people try to exit at the same time, the system can't keep up. The difference is that a traditional bank has regulators and deposit insurance to slow the panic. Algorithmic stablecoins have neither.

After the Terra collapse, purely algorithmic stablecoins mostly went out of use. What emerged instead were synthetic dollar designs, which work very differently.

USDe from Ethena is the most prominent example. When a user deposits ETH, Ethena holds that as a long position and simultaneously opens a short position on a crypto exchange. These two positions offset each other, so the dollar value stays stable regardless of price moves.

This is very different from an algorithmic stablecoin, which has no real collateral and relies entirely on mint-burn mechanics and market confidence to hold its peg.

Three things keep an algorithmic stablecoin running day to day:

Oracles

They feed real-world price data into the blockchain. The algorithm needs to know what the token is trading at before it can mint or burn. If that data is late or wrong, the system can make the wrong call at the worst time.

Arbitrage traders

They are the market's self-correction mechanism. When the price drifts from $1, traders step in to profit from the gap, and in doing so, push the price back toward the peg.

Governance tokens

Governance tokens help users influence how the protocol works, including supply rules and major updates. LUNA is one of the most recognized examples. But governance tokens are also a vulnerability. If confidence in the token falls, the system can become unstable.

Crypto is legal in India, but it does not have a clear place in the country's financial system. This means the rules for stablecoins and algorithmic stablecoins are still not well defined.

Under FEMA, stablecoins don't qualify as currency. They also don't meet the definition of a security. This leaves them in a grey area with no clear legal classification for cross-border use. Using them for international payments without authorization from the RBI may attract penalties under FEMA.

Plus, the RBI has been consistently opposed to private cryptocurrencies and stablecoins, viewing them as a threat to monetary sovereignty and India's payments infrastructure. In fact, as of mid-2026, India's crypto policy paper remains unpublished.

For tax purposes, stablecoins fall under Virtual Digital Assets. Gains are taxed at 30%, and a 1% TDS applies to transfers above specified thresholds.

Both algorithmic and fiat-backed stablecoins are built to stay stable in price, though they rely on completely different mechanisms.

The fall of TerraUSD changed how regulators view stablecoins globally. Here’s how different laws deal with algorithmic stablecoins today:

United States: GENIUS Act

The GENIUS Act is the first federal law in the US that specifically governs payment stablecoins. It requires full reserve backing, monthly reserve disclosures, and federal oversight for issuers above $10 billion. Stablecoins meeting these rules are not considered securities or commodities.

United States: STABLE Act

The STABLE Act is still a proposal. But one part of it stands out. It includes a two-year ban on new algorithmic stablecoins that rely on another token from the same issuer to hold their peg. This would effectively freeze the UST-style approach while long-term rules are developed.

European Union: MiCA

MiCA doesn't recognize purely algorithmic stablecoins at all. Unbacked models don't qualify under its framework. Under MiCA, a stablecoin must be fully backed, issued by a licensed entity, and redeemable at face value on request.

Most regulators don't consider algorithmic stablecoins to be stablecoins.

Under MiCA and the GENIUS Act, only fully backed stablecoins qualify as one. Algorithmic models don't meet that bar, so they fall outside these frameworks entirely and get treated as standard crypto assets instead.

A token classified as a crypto asset faces different rules around how it can be held, reported, and capitalized compared to a recognized payment stablecoin. The GENIUS Act even creates a separate category called "endogenously collateralized stablecoins" to handle this class.

Algorithmic stablecoins come with a few benefits:

Decentralization

No single company or bank controls the system. Everything runs on open-source smart contracts that anyone can read and verify. There's no central issuer that can freeze your funds or get shut down by regulators.

Capital efficiency

Since they do not require full reserve backing, they can grow without locking up large amounts of capital. This makes them appealing for high-volume DeFi use cases such as lending and payments.

Alignment with crypto's original vision

They are permissionless, borderless, and free from centralized control, which is what cryptocurrency was designed to be.

There are many ways algorithmic stablecoins can fail:

• Smart contract bugs can be exploited to drain liquidity or break the peg.
• Oracle failures cut off the accurate price data that the protocol needs to function.
• Market volatility can trigger a depeg in minutes, and if redemption pressure builds faster than the system can handle, recovery is unlikely.
• The dual-token model creates one major risk. Once the secondary token collapses, the stablecoin no longer has anything supporting its value.

DAI is not a purely algorithmic stablecoin. But it isn't purely collateral-backed either. It sits somewhere in between.

mint DAI, users lock up cryptocurrency worth more than the DAI they want to borrow. MakerDAO requires at least 150% collateral. This means to mint $100 worth of DAI, you'd need to lock up $150 worth of crypto. If the collateral value drops below the minimum ratio, the position gets liquidated automatically.

When users repay their loans, the DAI they return is burned, and their collateral is released. This mint and burn cycle is what keeps DAI's supply tied to actual collateral rather than just market sentiment.

The algorithmic side comes in through governance. MKR token holders can vote on collateral requirements, interest rates, and the types of assets accepted as collateral. These decisions directly influence DAI's supply and stability.

Algorithmic stablecoins also come with some risks:

No safety net

There are no reserves backing an algorithmic stablecoin. If the peg breaks, there's nothing to redeem against and nothing to slow the fall.

Smart contract risk

The whole system lives in code. A bug or a flawed design assumption can destabilize everything, regardless of market conditions.

Oracle dependency

Accurate price data is critical to the protocol's operation. When that data is wrong or delayed, the system can make incorrect minting or burning decisions at exactly the wrong time.

The stablecoin market is growing rapidly. And so is the regulatory scrutiny. Before engaging with any algorithmic stablecoin, make sure to check the following:

• Reserve quality

Does the stablecoin have the backing it promises? Look at the assets in reserve, how often reserve reports are published, and whether token holders have a direct redemption option.

• Redemption mechanics

Can you redeem quickly under stress? Or are there gates and delays built in?

• Issuer concentration

Two issuers account for nearly 90% of the global stablecoin market. Over-reliance on one can create operational risk if something goes wrong.

• Wallet and custody controls

Who can initiate transfers? Who approves them? And what happens if something goes wrong?

• Regulatory standing

Is the stablecoin recognized under MiCA, the GENIUS Act, or other frameworks relevant to your jurisdiction?

Given the compliance complications around crypto payments in India, many Indian businesses stick to traditional cross-border payment routes. Xflow is built specifically for this.

It lets freelancers, SMBs, and enterprises receive international payments in 25+ currencies through a virtual foreign currency account. Payments settle to your Indian bank account within one business day, and every transaction comes with an eFIRA issued automatically by an RBI-authorized bank.

With Xflow, you also get:

• FX rates linked to mid-market rates with no hidden fees
• No transaction limits
• An ISO 27001 and SOC 2 certified platform

Algorithmic stablecoins are one of crypto’s most ambitious ideas. At the same time, their past failures cannot be overlooked. Most purely algorithmic models have either collapsed or quietly shifted to hybrid designs that rely on some collateral.

For Indian businesses, the more pressing question isn't whether algorithmic stablecoins will eventually work. It's how to move money across borders today, compliantly and efficiently.

That's where Xflow comes in. Book a demo to see how Xflow helps Indian businesses receive international payments with full compliance, next-day settlements, and zero hidden fees.