Algorithmic vs Fiat-Backed: Analyzing Stablecoin Risks

In a market that sold itself on “digital dollars,” one spectacular collapse shattered the illusion of safety. TerraUSD (UST), once valued at more than $18 billion, lost its peg and imploded alongside LUNA, erasing tens of billions of notional wealth almost overnight.

On one side of the spectrum, an algorithmic experiment saw a $40 billion ecosystem evaporate in a matter of days; on the other, a leading fiat-backed stablecoin briefly traded as low as $0.87 when its reserves were caught up in the failure of Silicon Valley Bank, proving that design choice alone does not insulate “stable” assets from market runs.

Together, these episodes exposed stablecoins as both a powerful piece of financial plumbing and a potential new source of contagion. Algorithmic tokens that rely purely on code and endogenous collateral lack the buffers of overcollateralized or fiat-backed models, yet even the latter can face severe systemic risks when reserves are opaque, concentrated, or slow to move in a crisis.

As regulators, institutions, and everyday users look to rebuild trust, the crucial question is not whether stablecoins survive, but which designs deserve to. Comparing algorithmic and fiat-backed models through a risk lens reveals sharp trade-offs: capital efficiency versus resilience, decentralization versus dependence on banks and money markets, programmability versus regulatory clarity. Understanding these structural choices helps explain why some pegs snap violently while others wobble and recover.

In this article, we will unpack the mechanics, run dynamics, and reserve risks behind algorithmic and fiat-backed stablecoins so you can judge which models are actually built to last.

Mechanism Design of Algorithmic and Fiat Backed Stablecoins

When Terra’s ecosystem unraveled, Over $40 billion in UST and LUNA value vanished, and the core reason wasn’t “bad luck” but how its peg mechanism was wired. The contrast with fiat-backed designs is stark: their promises live or die on off-chain reserves and redemption rights, not reflexive token loops.

At the broadest level, stablecoins are crypto tokens engineered to track an external reference such as a fiat currency, commodity, other cryptocurrencies, or algorithmic rules. Within that universe, the key divide is between coins that hold traditional assets off-chain to back every token in circulation and coins that rely on algorithmic incentives and endogenous collateral to simulate that backing on-chain. These contrasting architectures create very different failure modes when markets get stressed.

In a typical fiat-backed design, a centralized issuer accepts deposits in dollars (or other fiat) and issues an equivalent number of tokens, holding the proceeds in cash, cash equivalents, or short-term government securities so that its liabilities as fiat-backed stablecoins are matched by high-quality reserves. Users can mint by sending fiat to the issuer and redeem at par, creating an arbitrage loop: if the token trades below the peg on exchanges, traders can buy it cheaply, redeem for full-value fiat, and pocket the spread, which helps pull the market price back toward the target. In practice, that mechanism ties the on-chain price tightly to the quality, liquidity, and accessibility of the off-chain reserve pool.

Algorithmic models flip this logic. Rather than holding external assets, they try to stabilize value by dynamically expanding and contracting token supply in response to market conditions, for example minting new units when the price trades above the peg and incentivizing users to burn the stablecoin in exchange for a separate governance token when it trades below. In calm markets, this can mimic the effect of a reserve, with the governance token absorbing volatility; in stress, however, mass selling of the stablecoin forces more governance tokens to be issued, driving down their price and undermining the very collateral base meant to support the system. Once that feedback loop takes hold, the value of governance tokens can crater, a ‘death spiral’ in which both tokens rapidly lose credibility and market value.

Mechanism design, in other words, is destiny: fiat-backed coins externalize risk to their banking and reserve arrangements, while algorithmic coins internalize it in code-driven monetary policy that can either dampen volatility or accelerate collapse under pressure.

Key Takeaways:

• Fiat-backed stablecoins rely on centralized issuers that hold traditional assets off-chain and use mint/redeem arbitrage to keep market prices close to the peg.
• Algorithmic stablecoins adjust supply based on price signals and often lean on a separate governance token to absorb volatility, creating complex feedback loops.
• When stress hits, fiat-backed designs hinge on reserve quality and access, while algorithmic designs are vulnerable to reflexive “death spirals” if confidence in the governance token evaporates.

Run Dynamics and Death Spirals in Algorithmic Stablecoins

During Terra’s collapse, LUNA’s circulating supply exploded to over 6 trillion tokens in a matter of days as the system kept printing governance coins to meet redemptions, while a newer algorithmic stablecoin, USDe, briefly traded around $0.60 after a single large leveraged position was liquidated on Binance. Those two events, separated by time and design tweaks, highlight how quickly “stable” algorithms can unravel once everyone heads for the exit at once.

These collapses are not black swans; they are the natural consequence of how most algorithmic pegs are structured. When holders rush to redeem and the protocol reacts by issuing ever more governance or “backing” tokens, you get a classic death spiral: falling prices force the system to mint additional tokens, which pushes prices lower still and entices even more redemptions. Liquidity disappears precisely when it is needed most, so the stabilizing mechanism that worked during calm conditions becomes a reflexive accelerator of panic.

Terra’s mint–burn arbitrage loop shows this reflexivity in action. As UST slipped below its dollar target, traders could burn UST to mint LUNA and sell that LUNA on the open market, capturing the spread and, in theory, helping restore the peg. But once sell pressure on LUNA overwhelmed demand, each unit of UST redeemed required more newly minted LUNA, driving LUNA’s price down further and forcing still larger issuances. The loop flipped from “stabilize the peg” to “hyperinflate the backing asset,” vaporizing the very collateral base that UST depended on.

USDe’s depeg followed a similar script despite using a more sophisticated delta-neutral hedging strategy. High, “risk-free” yields attracted capital, leverage amplified positions, and when a large holder was forced to unwind, the resulting liquidations cascaded through the system, dumping collateral and eroding confidence in the peg. As analysts at PANews point out, both Luna and USDe show how generous yields, leverage, and endogenous collateral create a fragile equilibrium that can rapidly collapse into a doom loop of selling, margin calls, and further devaluation once stress hits.

Understanding these run dynamics is essential for anyone evaluating algorithmic stablecoins: the same feedback loops that offer elegant stability in tranquil markets can turn into self-reinforcing collapse mechanisms when sentiment shifts.

Key Takeaways:

• Algorithmic stablecoins are prone to runs because their stabilization tools often require printing more governance or backing tokens exactly when market demand for those tokens is collapsing.
• Terra’s UST and newer designs like USDe show a recurring pattern: high yields attract leveraged capital, then redemptions and liquidations trigger hyperinflation of the backing token and a rapid loss of the peg.
• Evaluating any algorithmic stablecoin means stress-testing its redemption and backing mechanisms under mass-exit scenarios, not just assuming that on-paper arbitrage logic will hold in a crisis.

Reserve Quality and Transparency Risks in Fiat Stablecoins

One of the clearest illustrations of scale in fiat-backed stablecoins is Tether, which has reported holding roughly $86 billion in assets including U.S. Treasuries, precious metals, bitcoin, other investments and secured loans to back a similar amount of USDT in circulation. When that much notional “digital cash” depends on a private balance sheet, the quality and transparency of the underlying reserves become systemic questions, not just technical details.

For fiat-backed designs, the peg no longer depends on algorithmic burn-and-mint loops but on whether every token can be redeemed for cash from a pool of safe, liquid assets. Regulators are increasingly explicit about what “safe and liquid” means: the Basel Committee has proposed that only stablecoins largely invested in short-maturity, high-credit-quality instruments whose reserves are legally insulated from operator or custodian bankruptcy should receive favorable capital treatment on bank balance sheets. Circle’s public filing gives a concrete example, explaining that USDC reserves sit primarily in a dedicated government money market fund and cash accounts at global systemically important banks, held in segregated structures that cannot be lent out or used for corporate purposes.

Not every issuer meets that emerging gold standard, and the gaps usually show up first in disclosure. As legal analysts at Coinlaw note, some major stablecoins still have not completed full external audits, instead relying on attestations that arrive weeks after the reporting period and often omit critical details such as the identity and jurisdiction of reserve custodians. That combination of complex reserve portfolios and delayed, partial reporting makes it very hard for traders or institutions to judge, in real time, how a coin might behave if redemptions suddenly spike.

Transparency also interacts with legal structure: Circle, for instance, emphasizes that its segregated reserve accounts are held for the exclusive benefit of stablecoin holders and cannot be pledged or rehypothecated, which should improve recoveries in an insolvency event. For treasurers using stablecoins for payroll, trading, or cross-border settlement, that difference between bankruptcy-remote cash and a claim on a risky, opaque pool of assets can determine whether a market shock is a minor volatility event or a full-blown liquidity crisis.

For anyone treating fiat-backed stablecoins as cash-equivalents, the real diligence task is not simply asking whether a coin is “fully backed,” but interrogating what backs it, how quickly it can be liquidated, and how clearly that information is disclosed.

Key Takeaways:

• In fiat-backed stablecoins, risk migrates from algorithmic design to the composition, liquidity, and legal safeguards of the reserve portfolio.
• Regulators increasingly expect reserves to be short-term, high-credit-quality and bankruptcy-remote, a standard exemplified by USDC’s segregated money market fund and bank cash structure.
• Gaps in transparency—limited attestations instead of full audits, reporting delays, and opaque custodians—turn reserve quality into a guessing game and can accelerate runs when market sentiment turns.

Evaluating Long Term Viability of Emerging Stablecoin Models

Stablecoins are no longer just speculative tools; they are everyday money for people and businesses. The IMF notes that stablecoin firms already serve millions of users who move value across borders at low cost, and that dollar-pegged tokens have become a financial lifeline in some high‑inflation economies, which makes the question of which models can endure far more than an academic debate.

From a design standpoint, most new proposals fall into a small number of archetypes rather than a messy continuum. One empirical study classifies stablecoins into four types based on how they source and manage collateral, then tests how each behaves under different stress scenarios. Its core finding is that long‑term stability depends less on marketing labels like “algorithmic” or “fiat-backed” and more on the quality, behavior, and governance of collateral and the rules that govern redemptions when markets are under pressure.

A companion paper builds a game-theoretical model of stablecoin pricing and shows that coins can settle into very different equilibria depending on architecture and incentives. In this framework, designing truly resilient, long-lived stablecoins is explicitly described as a “hard challenge”: the same mechanism that dampens volatility in normal times can amplify it if users suddenly doubt the peg and rush to exit. For treasurers, fintechs, or payroll platforms choosing settlement rails, this implies a practical due‑diligence checklist: interrogate how the coin behaves in stress scenarios, how quickly collateral can be liquidated, whether redemptions can be paused or gated, and how well incentives align with preserving the peg rather than chasing yield.

Emerging hybrid models offer one route forward. The DAI stablecoin, for example, combines crypto‑collateralization with algorithmic policies, and researchers have now encoded its rules into a formal, logic‑based framework to simulate stability and probe for vulnerabilities before they are exploited in the wild. In parallel, global authorities led by the IMF and FSB have issued comprehensive guidance on how to manage the macroeconomic, financial stability, legal and integrity risks posed by stablecoins, signaling that designs which cannot meet basic standards for collateral, disclosure, and governance are unlikely to gain regulatory acceptance at scale.

Taken together, the research and policy trajectory point in the same direction: the stablecoins that are most likely to endure are those that pair robust, well-governed collateral with mechanisms that remain credible under stress and are compatible with emerging global rules, whether they are fiat-backed, crypto‑collateralized, or carefully engineered hybrids.

Key Takeaways:

• Long-term viability hinges on collateral origin and management, redemption rules, and incentive design, not just on whether a coin is branded “algorithmic” or “fiat-backed.”
• Academic models show that stablecoins can flip between stable and depegged equilibria as user expectations change, which makes stress behavior and governance as important as day‑to‑day price tracking.
• Hybrid and fiat-backed models that combine high-quality collateral, transparent mechanisms, and alignment with evolving regulatory frameworks are best positioned to serve real-world use cases like cross‑border payments and payroll over the long run.

Put Stablecoin Risk Insights to Work in Your Payroll Stack

If this analysis underscored anything, it’s that stablecoin design and reserve quality aren’t abstract concerns—they directly affect whether your team gets paid on time, at full value, when markets are stressed. Bitwage was built around that reality. As a global payroll platform with a 10‑year, zero‑breach record, Bitwage lets you leverage compliant stablecoin payroll and crypto payouts—alongside local fiat—without taking on the operational or counterparty risk of experimental algorithms. You choose risk‑managed assets like leading fiat‑backed stablecoins or local currencies; Bitwage handles the rails, W‑2–compliant reporting, and same‑day settlement for teams in nearly 200 countries.

Don’t wait for the next depeg or banking shock to discover weaknesses in your payment stack. Future‑proof your compensation strategy by moving to a battle‑tested crypto payroll and stablecoin payroll solution that’s already processed over $400 million for 90,000+ workers worldwide. Explore how Bitwage can streamline your invoices, automate accounting, and give your global workforce faster, more predictable access to their earnings—whether in crypto, stablecoins, or fiat. Signup for Crypto Payroll today!