MEXC COO Says AI Must Remain a Co-Pilot in Crypto Trading

Artificial intelligence is rapidly reshaping crypto markets across risk management and liquidity optimization. 

However, according to MEXC Chief Operating Officer Vugar Usi, artificial intelligence (AI) should not replace human decision-making.

In an interview with CCN, Usi said AI plays a central role in modern exchanges, especially in high-stakes financial environments where accuracy and accountability matter most.

“AI is a great co-pilot. For me, AI should always have human supervision, whether for the smallest decisions or for large decisions that impact people’s lives,” he said.

Usi also framed MEXC as more than a crypto exchange, describing it as a multi-asset trading platform where users can access tokenized stocks, commodities, real-world assets, and more than 3,000 crypto projects.

This broader scope increases the complexity of operations and reinforces the need for AI systems that can handle scale while remaining under human control.

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AI Moves From Add-on To Core Exchange Infrastructure

Many platforms still treat AI as a secondary feature. MEXC takes a different approach by integrating AI into its core systems.

Usi explained that the exchange uses AI across three main areas:

• Operational processes: AI supports user verification, Anti-Money Laundering (AML), and Know Your Customer (KYC) procedures, improving speed and efficiency.
• Liquidity management: AI analyzes order books and helps coordinate liquidity across fragmented markets.
• User tools: AI-powered bots and analytics assist users in making trading decisions.

“With 40 million users… it is millions of transactions every day,” Usi said, highlighting the need for automation at scale.

He added that AI enables exchanges to process vast volumes of activity that would otherwise require significant manual oversight.

In practice, this means faster onboarding, quicker compliance checks, and more responsive systems during periods of high trading demand.

Data Quality and Ethics Remain Critical Challenges

AI systems depend on large datasets, but crypto markets present challenges due to fragmentation, manipulation risks, and inconsistent data quality.

Usi emphasized the importance of ethical data use and privacy protection.

“It is very important… that no user data is fed to the AI or it is well masked,” he said.

He also pointed to growing concerns around data misuse, including cases where companies allegedly trained AI models using unauthorized or deceptive methods.

At the same time, AI can strengthen security. MEXC uses AI to detect deepfakes and prevent attempts to bypass identity verification systems.

“AI… can combat other AIs and deep fake agents that try to bypass the process,” Usi said.

Usi noted that AI systems trained on verified identity data can outperform humans in detecting fraud attempts.

In identity checks, AI can analyze facial patterns, motion consistency, and document authenticity in real time, reducing the success rate of synthetic identities and manipulated media.

Key Risk: Overreliance on AI Decision-Making

Despite its capabilities, AI introduces new risks, particularly when used without supervision.

Usi warned that AI systems can replicate errors at scale, especially in trading environments.

“It takes only one wrong decision… and there were hundreds of liquidations because just one bot made one mistake,” he said.

He stressed that AI lacks judgment and emotional awareness, which remain essential in financial decision-making.

“When something goes wrong, that is where human interference comes into place,” he added.

He compared low-risk AI use cases, such as drafting emails or generating images, with high-stakes financial decisions. In trading or healthcare scenarios, even a single incorrect output can have serious financial or human consequences, making oversight non-negotiable.

AI Improves Liquidity but Does Not Replace Human Control

Crypto liquidity remains fragmented across exchanges and markets. AI helps address this by processing large volumes of data and improving execution speed.

However, Usi clarified that AI does not independently control liquidity decisions.

“AI doesn’t decide where to pull the liquidity from… it looks into whether it is… as per the standard,” he said.

AI also enhances risk management by analyzing wallet activity, detecting illicit transactions, and identifying potential compliance issues in real time.

He explained that AI can review hundreds of thousands of transactions in seconds, far beyond human capacity. This allows exchanges to flag suspicious wallets, detect exposure to illicit funds, and maintain higher compliance standards without slowing down operations.

Extreme Volatility Exposes AI Limitations

AI systems perform well under normal conditions but struggle during rare or extreme market events.

“When it comes to extreme volatility… AI is performing worse there because AI needs those particular scenarios to learn,” Usi said.

He compared AI to an intern that requires supervision, especially during unpredictable situations.

“AI is a good co-pilot, but AI is a bad pilot,” he said.

Human oversight acts as a safeguard, stepping in when models fail or produce unreliable outputs.

Usi added that human operators act as both accelerators and brakes. When AI performs well, humans can scale its use. When AI fails, humans must intervene quickly to prevent cascading errors across trading systems.

Fairness Debate Grows as AI Tools Create Advantages

AI-driven trading tools raise concerns about market fairness. Access to advanced technology can give certain participants a competitive edge.

Usi acknowledged this imbalance but framed it as a broader feature of technological progress.

“Whoever has access to better tools is able to perform better,” he said.

MEXC aims to reduce this gap by offering AI tools to retail users, with more than 99% of its traders classified as individual participants.

“Our goal is to bring this institutionally great trading AI support to our users,” he added.

He also highlighted cost concerns, noting that AI infrastructure requires significant computing power and energy. In some cases, the cost of running AI systems may outweigh the benefits unless platforms operate at scale.

Regulation Complexity Increases Across Global Markets

Operating across more than 170 jurisdictions, MEXC faces a highly fragmented regulatory landscape.

Usi highlighted AI’s role in managing compliance at scale by adapting platform features to local rules.

“AI… can replicate these different parallel realities,” he said.

For example, leverage limits or product access can vary depending on regional regulations, and AI helps automatically enforce these differences.

He also warned that increasing regulation could slow innovation, especially for smaller firms.

“Sometimes legal frameworks are very complicated… that might kill innovation,” he said.

He added that AI could reduce compliance costs by minimizing the need for large legal teams across jurisdictions. This could help startups compete while still meeting regulatory requirements in multiple markets.

AI and Crypto To Evolve Together Over the Next Five Years

Looking ahead, Usi expects both AI and crypto to evolve rapidly, making long-term predictions difficult.

He expressed optimism that AI will improve access to services, education, healthcare, and financial tools.

“I hope… AI gives better education or medical advice for humans to make human life better,” he said.

At the same time, he cautioned against focusing only on efficiency and profit.

“If we think about how to just maximize profit… I don’t think that this is the best place,” he added.

Usi also pointed to the human element in markets, noting that emotions, loyalty, and social behavior still influence trading decisions. AI lacks this layer, which means markets cannot become fully automated without losing key behavioral dynamics.

Human Oversight Will Define AI’s Role in Crypto

AI continues to transform crypto markets, improving efficiency, risk management, and accessibility. However, its limitations remain clear, especially in volatile or high-risk scenarios.

Human supervision remains essential to ensure responsible use, prevent systemic errors, and maintain ethical standards.

As Usi concluded, AI should support decision-making, not replace it, especially when financial outcomes affect real lives.

About the Author

Dr. Lorena Nessi

Dr. Lorena Nessi is an award-winning journalist and media technology expert with 15 years of experience in digital culture and communication. Based in Oxfordshire, UK, she combines academic insight with hands-on media practice.

She holds a PhD in Communication, Sociology, and Digital Cultures, and an MA in Globalization, Identity, and Technology.

Lorena has taught at Fairleigh Dickinson University, Nottingham Trent University, and the University of Oxford. She is a former producer for the BBC in London, with additional experience creating television content in Mexico and Japan.

Her research focuses on digital cultures, social media, technology, capitalism, and the societal impact of blockchain innovation.

She has written extensively on digital media and emerging technologies, with her work featured in both academic and media platforms. Her Web3 expertise explores how blockchain technologies shape culture, economics, and decentralized systems.

Outside of work, Lorena enjoys reading science fiction, playing strategic board games, traveling, and chasing adventures that get her heart racing. A perfect day ends with a relaxing spa and a good family meal.

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