Crypto Companies Leverage AI Despite Ongoing Challenges

Why Companies Are Combining Crypto with AI

The integration of artificial intelligence with blockchain technology has become a strategic priority for crypto companies seeking to enhance operational efficiency and deliver superior user experiences. Industry leaders are exploring innovative ways to leverage AI capabilities across various aspects of their business operations, from compliance and risk management to customer service and product development.

Jacqueline Burns-Koven, head of cyber threat intelligence for Chainalysis – a blockchain analysis firm – explained that the company has started thinking about ways to use AI to make compliance, risk, investigations and growth products better for customers. "Like any business, we stand to benefit by utilizing AI to improve how we work across the business; by making it faster and more efficient," Burns-Koven stated. This approach reflects a broader industry trend where AI is being deployed to streamline complex processes and reduce operational overhead.

In the realm of cryptocurrency taxation, AI is transforming how users handle their tax obligations. Crypto tax software provider ZenLedger announced a partnership with april – an AI-powered financial company – to use AI to simplify the tax filing process for users. Pat Larsen, co-founder and CEO of ZenLedger, explained that ZenLedger's new product will leverage april's technology to route taxpayers through a single flow, combining federal and state requirements, and then deciding which question to ask next. "This is in contrast to traditional tax filing software that asks questions of the user in the order the forms are completed, and then parses out federal and state forms into separate sections, often duplicating the same questions in each," Larsen said. This intelligent approach significantly reduces the time and complexity involved in crypto tax reporting.

Daniel Marcous, CTO and co-founder of april, elaborated on the technical implementation behind this innovation. He explained that AI has been instrumental to april's ability to build a tax product covering many common tax scenarios, including income from crypto and digital assets. According to Marcous, april uses a process called "tax-to-code" in which large language models have been trained to read tax documents and then turn those into software code which is then reviewed and edited by a team of tax engineers. This hybrid approach combines the efficiency of AI with the expertise of human professionals to ensure accuracy and compliance.

AI-Powered DeFi Applications

Artificial intelligence is also helping power a number of decentralized finance (DeFi) use cases, opening new possibilities for how digital assets are valued, traded, and managed. Nick Emmons, co-founder and CEO of Upshot – an AI infrastructure company – described how his company is building a decentralized network where different AI models can learn from each other. According to Emmons, having models learn off each other will create a meta intelligence across an AI-powered network. In turn, this will make networks more performant and intelligent compared with individual models being used in isolation.

Emmons explained that Upshot's AI model is powering a number of DeFi use cases that were previously impractical or impossible to implement. For example, he explained that AI can create efficiencies for price feeds for long-tail crypto assets, or digital assets that don't often trade but exist in liquid settings. Traditional pricing mechanisms struggle with these assets due to infrequent trading activity, but AI can analyze multiple data sources to generate reliable valuations. He stated:

"AI becomes a useful tool for being able to produce more frequent price updates based on different information, not just an asset changing hands. This means that we can now start to bring a much larger universe of assets into the DeFi design space."

To put this in perspective, Emmons explained that Upshot will soon introduce "watch perps" generated by AI-enabled watch feeds. This innovation demonstrates how AI can create markets for previously illiquid assets. He said:

"An individual watch is incapable of producing a real enough time feed to build a market around it. AI models can process a lot of information at once, so you can start to produce highly accurate and high frequency price feeds to turn digital assets into on-chain, tokenized representations. This will expand the universe of digital assets."

AI-Enhanced Yield Strategies

Additionally, Emmons pointed out that AI-powered DeFi vaults are coming to fruition, representing a significant advancement in automated investment strategies. A DeFi vault acts as a pool of funds with an auto-compounding strategy that manages and performs tasks based on predefined on-chain conditions. Yet Emmons noted that this is problematic given that most on-chain activity is limited when it comes to compute power. "As such, the yield a user can generate is limited," he explained. This computational constraint has historically restricted the sophistication of on-chain strategies.

In order to solve this problem, Emmons noted that AI models can be applied to make sense of information more efficiently. "AI can be used to codify strategies that can be brought on-chain in the form of vaults. This can then be used for market making and more." By processing complex data off-chain and implementing optimized strategies on-chain, AI enables more sophisticated yield generation mechanisms.

Although this use case is still in its infancy, RoboNet is an AI-powered DeFi protocol for long-tail and fungible asset markets. RoboNet is powered by Upshot and allows for the creation of on-chain vaults managed by machine learning models that generate yield through automated liquidity optimization strategies. This represents a practical implementation of AI-driven DeFi that could serve as a model for future developments in the space.

Challenges Combining AI with Crypto

While AI can help crypto products perform more efficiently, there are still a number of challenges to consider that must be addressed to ensure safe and effective implementation. The intersection of these two rapidly evolving technologies presents unique risks and concerns that require careful attention from developers, regulators, and users alike.

Trust and Transparency Issues

For example, Emmons pointed out that when AI is leveraged for building DeFi protocols, the creators behind those models need to be trusted, otherwise a number of issues could occur. The black-box nature of many AI systems creates potential vulnerabilities in financial applications. He said:

"Bias and manipulation can arise, which is why it's important to reimagine the AI stack in decentralized form factors. Different models can keep other models in check to create less bias and a more transparent source of intelligence."

Emmons explained that zero-knowledge (ZK) proofs can also help verify machine learning models, providing a cryptographic guarantee of model integrity. "Upshot has released a product like this where we verified the output of our flagship price prediction model inside a ZK circuit. This provides assurance and computational integrity for permissionless protocols." This approach represents a promising direction for addressing trust issues in AI-powered crypto applications.

Marcous added that he believes generative AI working alongside tax experts and engineers mitigates risk since a human is involved in the process. "At april, we conduct a rigorous testing process on the entirety of the product and have to pass tests with the Internal Revenue Service and state authorities before launching," he said. This human-in-the-loop approach ensures that AI outputs are validated by domain experts before being deployed in production systems.

Regulatory Uncertainty

While these tactics may be helpful, the lack of regulations around the use of AI will likely present ongoing challenges for the crypto industry. For instance, understanding whether or not AI is being applied for the best interest of users versus investors or the creators of machine learning models remains difficult to determine. This opacity creates potential conflicts of interest and raises questions about accountability when AI systems make decisions that affect users' financial outcomes.

Due to this, certain countries have started to establish organizations to enforce AI regulations. For example, the president of the United Arab Emirates and ruler of Abu Dhabi, Sheikh Mohamed bin Zayed Al Nahyan, issued a law to establish the Artificial Intelligence and Advanced Technology Council. An announcement from the Abu Dhabi government noted that, "the council will be responsible for developing and implementing policies and strategies related to research, infrastructure and investments in artificial intelligence and advanced technology in Abu Dhabi." This represents one of the first comprehensive governmental frameworks for AI oversight.

United States Securities and Exchange Commission Chair Gary Gensler has also warned about the dangers that AI could pose to the traditional financial sector. Given this, more regulatory clarity around AI will likely be implemented in the U.S. in the future. The regulatory landscape for AI in finance is expected to evolve significantly in coming years as authorities grapple with the implications of these technologies.

Future Outlook

All of these developments are important, as Emmons believes that AI will eventually be incorporated into every critical function of society. In the meantime, he pointed out that the crypto sector will likely incorporate forms of AI that have already been implemented in traditional financial systems. He explained:

"This is because crypto is a financial innovation, so this type of AI can be more conducive with financial applications. Also, classical types of machine learning models are more attractive and compatible with these verifiable form factors, so cryptographic tooling that can be built around those will be able to come online faster than generative AI models."

This pragmatic approach suggests that the crypto industry will adopt proven AI techniques from traditional finance before experimenting with more cutting-edge AI technologies. As the technology matures and regulatory frameworks develop, the integration of AI and crypto is expected to deepen, potentially transforming how digital assets are created, traded, and managed.

FAQ

Why do crypto companies use AI technology? What are the specific application scenarios?

Crypto companies leverage AI for fraud detection, risk management, trading optimization, and market analysis. AI enhances security through pattern recognition, automates transaction monitoring, improves price prediction accuracy, and personalizes user experiences. These applications increase operational efficiency and trading volume substantially.

How is AI applied in cryptocurrency trading and risk management?

AI analyzes historical market data to predict risks and identify trading opportunities. It detects anomalies and suspicious patterns in real-time, enhancing security. Machine learning models optimize portfolio decisions and reduce potential losses through automated risk assessment and mitigation strategies.

What are the main challenges and risks crypto companies face when leveraging AI?

Crypto companies encounter technical complexity in AI implementation, regulatory uncertainty around AI governance, data security vulnerabilities, market adoption barriers, and talent scarcity in blockchain-AI expertise. These factors complicate development timelines and increase operational costs significantly.

What is the role of AI in detecting cryptocurrency fraud and anti-money laundering (AML)?

AI monitors transactions in real-time to identify suspicious activities and patterns, enhancing fraud detection and AML compliance. It automates reporting processes, reduces regulatory burdens, and improves operational efficiency for crypto businesses.

What role does AI technology play in security protection at cryptocurrency exchanges?

AI detects and prevents security threats in real-time, automates threat response, identifies fraudulent transactions, and strengthens overall exchange security through continuous monitoring and anomaly detection.

What are the requirements and restrictions that regulators impose on crypto companies using AI?

Regulators require crypto companies to establish AI-adapted compliance frameworks for trading and risk management. They mandate transparency and algorithmic oversight while following a 'minimal effective regulation' approach to foster innovation in AI-driven financial services.

What are the advantages and disadvantages of AI-driven crypto projects compared to traditional projects?

AI-driven crypto projects offer superior efficiency through automation and dynamic market adaptation, enabling faster decision-making and optimized operations. However, they face higher technical complexity, implementation risks, and potential vulnerabilities in AI algorithms that traditional projects may avoid.

What data privacy and compliance challenges does AI face in the cryptocurrency industry?

AI in crypto faces data privacy risks from unauthorized data reuse, security breaches exposing sensitive information, and compliance challenges with regulations like GDPR and CCPA. Companies must implement strict data governance, encryption, and transparent user consent practices to protect personal data and maintain regulatory compliance.