The fall of Mantra (OM) and why artificial intelligence (AI) could have prevented the 90% token crash

The fall of Mantra (OM), the native token of the layer-1 real-world asset blockchain Mantra, shook the crypto market on April 13. Within hours, the asset saw its market cap plunge from over $6 billion to around $500 million.

In a market already scarred by billion-dollar collapses, the collapse of Mantra’s native asset proved yet again that hacks aren’t the only enemy to the industry—crypto has been crippled by negligence. The team behind Mantra blamed “forced liquidations” for the 90% token crash, which is only half of the story.

As more data surfaces, it’s becoming clear that the collapse wasn’t just a case of unfortunate timing or high market volatility. It was a preventable disaster that had many catalysts, like overleveraged positions, weak liquidity, and various gaps in its automated risk management systems.

Ironically, artificial intelligence, the technology that crypto evangelists have been praising over the last three years, could have predicted, flagged, and even prevented this crash, had it been implemented properly.

AI-driven liquidity stress testing

The problem with traditional financial stress testing is that it is designed for stable, regulated markets and conventional assets like stocks and bonds, where extreme volatility is rare. Cryptocurrencies, on the other hand, operate in a different reality where wild price swings and sudden liquidity crashes are pretty common and part of the market game. Legacy risk frameworks that rely on historical patterns fail to capture these shocks.

AI-driven stress testing offers a dynamic alternative. Instead of relying on static historical data, machine learning models adapt to real-time conditions, analyzing market sentiment, on-chain metrics, and liquidity patterns.

A new method called kurtosis-based stress testing focuses on reducing the risk of extreme outlier losses, precisely the “fat tail” events that characterize crypto market failures. This technique can help firms in “less predictable, high-impact” events like the recent Mantra and the 2022 Terra (LUNA) crashes. During the Terra collapse in 2022, traditional risk models failed because they didn’t anticipate how quickly a stablecoin de-peg could spiral into a $60 billion wipeout.

The research shows that portfolios designed to reduce extreme risk swings delivered a 491% return with the kurtosis model, beating the simpler ‘buy-and-hold’ approach at 426% and even outperforming those built around traditional Sharpe ratio strategies, with a 384% return.

A high kurtosis value indicates a higher probability of extreme volatility. In crypto, these events aren’t anomalies—they’re part of the landscape.

Mantra’s exposure to thin weekend liquidity and token concentration could have been flagged well in advance with AI-powered stress testing methods, offering stakeholders a window to act before catastrophe struck.

Tracking and flagging movements with AI

Blockchain’s transparency is its greatest strength, yet monitoring millions of transactions manually is impossible. This is where AI excels. Autonomous AI agents can continuously scan on-chain activity and flag unusual patterns that might indicate impending market manipulation, all without the need for human involvement.

In Mantra’s case, blockchain data analyzed after the crash revealed telling signs. Just days before the collapse, a wallet linked to Laser Digital reportedly transferred 6.5 million OM tokens to another wallet, which then sent them to OKX, where they were liquidated. An AI monitoring system could have detected these movements in real time, issuing immediate alerts to exchanges, regulators, and the broader community.

AI agents can distinguish routine market behavior from potential manipulations since they don’t just track transactions but also build behavioral profiles across wallet networks.

Predicting order book vulnerabilities

Perhaps the most direct way AI could have prevented the Mantra crash is through sophisticated order book analysis. Order books reveal the true health of a market, but their complexity demands more than just surface-level analysis.

Deep learning models, particularly Convolutional Neural Networks and Long Short-Term Memory networks, have proven to deliver promising results in forecasting price movements based on order book data. One study found that temporal CNNs can predict Bitcoin (BTC) price shifts with up to 76% accuracy.

AI-driven analysis of market depth would have highlighted the risk of significant slippage from large sell orders—conditions ripe for a cascading price collapse. Consequently, these models could have exposed Mantra’s fragility by identifying dangerously thin order books during weekend trading hours.

With the help of AI and deep learning models, crypto firms can implement dynamic safeguards like circuit breakers triggered by sharp price drops and structural weaknesses in liquidity to flag or prevent situations similar to Mantra.

Building a resilient crypto ecosystem with AI

While blockchain technology promises decentralization and transparency, it remains vulnerable without advanced AI-powered risk management systems that can process millions of transactions and flag suspicious patterns. The collapse of high-profile assets like Mantra and Terra has proven the need for these systems.

Financial institutions with crypto exposure must prioritize