TerraUSD Collapse: What Happened and Why It Still Matters

When TerraUSD, a popular algorithmic stablecoin pegged to the US dollar suddenly lost its $1 value in May 2022, it triggered the biggest crypto crash since 2018. Unlike Bitcoin or Ethereum, TerraUSD wasn’t backed by cash or reserves—it relied on a complex system of code and incentives to stay stable. That system failed in hours, dragging down its sister token, Luna, the volatile token designed to absorb TerraUSD’s price swings, which plummeted from over $100 to fractions of a cent. This wasn’t just a price drop—it was a systemic collapse that exposed how fragile some DeFi models really are.

The algorithmic stablecoin, a type of digital currency that uses smart contracts instead of collateral to maintain its value model promised to be more scalable than traditional stablecoins like USDT or USDC. But when users started cashing out, the mechanism couldn’t keep up. The arbitrage between TerraUSD and Luna broke down, triggering a death spiral. No central bank stepped in. No reserves were available. And over $40 billion in market value vanished in days. This event didn’t just hurt investors—it shook confidence in the entire crypto ecosystem. Regulators took notice. Exchanges delisted tokens. Projects that used similar models scrambled to rebuild trust.

Since then, the crypto world has been forced to ask hard questions: Can a stablecoin really work without real assets backing it? Are smart contracts enough to handle panic? And what happens when the incentives behind a token stop working? The TerraUSD collapse didn’t end algorithmic stablecoins—but it did change how we evaluate them. Today, every new stablecoin project has to prove it’s not just code, but also resilient under pressure. You’ll find posts here that dig into the aftermath: how exchanges like BitForex and TradeSatoshi collapsed right after, how fake airdrops exploded in the chaos, and how users got burned by platforms that claimed to be "safe" but had no real oversight. These aren’t just stories about failed tokens—they’re lessons in what happens when trust is built on assumptions, not facts.