According to recent statistics, the market size of artificial intelligence (AI) is expected to exceed $3 billion this year. Therefore, it is no surprise that many cryptocurrency-focused companies are starting to incorporate AI into their products.
Why are companies combining cryptocurrencies and AI?
Jacqueline Burns-Coven, head of cyber threat intelligence at blockchain analytics company Chainaloss, told Cryptonews that Chainaosis leverages AI to make compliance, risk, investigation and growth products better for customers. He said he is starting to consider ways to do so. “Like other companies, we can benefit from leveraging AI to improve the way we work across our business. By making it faster and more efficient,” says Burns Coven. said.
Crypto tax software provider ZenLedger also recently announced a partnership with april, an AI-powered financial company, to leverage AI to simplify the tax process for users. Pat Larsen, co-founder and CEO of ZenLedger, told Cryptonews that ZenLedger’s new product leverages APRIL’s technology to connect taxpayers to a single combined federal and state flow. He said he would route them and decide what questions to ask next. “This is in contrast to traditional tax preparation software, which asks users questions in the order the forms are completed and parses federal and state forms into separate sections, often with duplicate questions on each.” Larsen said.
Daniel Marcus, CTO and co-founder of april, told CryptoNews that AI has contributed to april’s ability to build tax products that cover many common tax scenarios, including income from cryptocurrencies and digital assets. He said he was contributing. Marcus said APRIL employs a process called “tax-to-code,” in which a large-scale language model (LLM) reads tax documents, converts them into software and his team of tax engineers. Trained to be reviewed and edited by. .
AI is also helping power many decentralized finance (DeFi) use cases. Nick Emmons, co-founder and CEO of AI infrastructure company Upshot, told Cryptonews that Upshot is building a decentralized network where different AI models can learn from each other. Emmons said having models learn from each other creates meta-intelligence across the AI-powered network. This makes the network more performant and intelligent compared to the individual models used.
Emmons explained that Upshot’s AI models power many DeFi use cases. For example, he explained that AI can streamline price feeds for long-tail cryptoassets, digital assets that are infrequently traded but exist in highly liquid environments. He said:
“AI becomes a useful tool that allows for more frequent price updates based on a variety of information, not just asset changes in ownership. This brings the larger asset world into the DeFi design space. That means we can start implementing them.”
To put this into perspective, Emmons explained that Upshot will soon be introducing “clock purp,” which will be generated by an AI-enabled clock feed. He said:
“Individual clocks cannot generate enough real-time time feeds to build a market. AI models can process a lot of information at once, so they begin to generate highly accurate and frequent price feeds. and turn digital assets into on-chain tokenized representations. This expands the world of digital assets.”
Furthermore, Emmons pointed out that AI-powered DeFi vaults are becoming a reality. A DeFi vault acts as a pool of funds with automated compounding strategies that manage and execute tasks based on predefined on-chain conditions. However, Emmons pointed out that this is problematic given that most on-chain activity is limited when it comes to computing power. “Therefore, there are limits to how much revenue users can generate,” he said.
To solve this problem, Emmons pointed out that AI models can be applied to understand information more efficiently. He said, “AI can be used to codify strategies that can be deployed on-chain in the form of vaults, which can be used for things like market making.”
Although this use case is still in its early stages, RoboNet is an AI-powered DeFi protocol for long-tail and alternative asset markets. RoboNet is powered by his Upshot, which enables the creation of on-chain vaults managed by machine learning models that generate revenue through automated liquidity optimization strategies.
Challenges of combining AI and cryptocurrency
Although AI can help make crypto products perform more efficiently, there are still many challenges to consider. For example, Emmons pointed out that when leveraging AI to build DeFi protocols, we need to trust the creators behind those models, or there can be a lot of problems. He said:
“Because of the potential for bias and manipulation, it is important to rethink the AI stack with a decentralized form factor. Different models suppress other models, reducing bias and creating a more transparent source of information. can do.”
Emmons explained that ZK proofs are also useful for validating machine learning models. “Upshot recently released a product like this that validates the output of our flagship price prediction model within the ZK circuit, which provides the guarantees and computational integrity of a permissionless protocol.”
Marcus added that he believes generative AI working in collaboration with tax professionals and engineers will reduce risk because humans are involved. “We put the entire product through a rigorous testing process in April, and it must pass tests from the Internal Revenue Service and state authorities before it can be released,” he said.
While these tactics may be helpful, the lack of regulation around the use of AI may pose ongoing challenges. For example, it remains difficult to understand whether AI is being applied in the best interest of users and investors, or the creators of machine learning models.
For this reason, some countries have begun to establish organizations to enforce AI regulations. For example, Sheikh Mohammed bin Zayed Al Nahyan, President of the United Arab Emirates and Ruler of Abu Dhabi, recently promulgated a law establishing the Artificial Intelligence and Advanced Technology Council (AIATC). “The council will be responsible for developing and implementing policies and strategies related to artificial intelligence and advanced technology research, infrastructure and investment in Abu Dhabi,” a statement from the Abu Dhabi government said.
U.S. Securities and Exchange Commission Chairman Gary Gensler also recently warned of the dangers that AI could pose to the traditional financial sector. With this in mind, regulations regarding AI will become clearer in the United States in the future.
All of these developments are important because Emmons believes that eventually AI will be embedded in every important function of society. On the other hand, he noted that the crypto sector is likely to incorporate forms of AI that are already implemented in traditional financial systems. He said:
“This is because cryptocurrencies are a financial innovation, so this type of AI may be more suitable for financial applications. Also, classical types of machine learning models are more attractive and Because they are compatible with verifiable form factors, cryptographic tools that can be built around them are likely to come online faster than generative AI models.”