AI and DeFi: A Deep Dive into the Future of Finance

Our deep-dive series into the convergence of AI and blockchain technology began by identifying the innovation these two technologies are unlocking, then explored some pitfalls and hurdles that stand in the way of mainstream success.

Now, let’s look at how AI and DeFi (decentralized finance) are colliding and what it means for the future of finance. In this post, we look at AI’s use cases across the DeFi spectrum, where it can be leveraged, and how the issues of data privacy, security, and scalability will need to be addressed before the full potential of AI and DeFi can be realized.

Since bursting onto the scene in 2017, DeFi builders have been build out decentralized versions of the traditional financial industry infrastructure. This development has opened up banking and critical financial products to anyone with an internet connection. 

The explosion of AI into mainstream popularity since 2022 has had a similar effect in almost all areas of software, although much of its true potential is yet to be realized. We are now beginning to witness the convergence between these two revolutionary technologies, which can potentially transform the financial sector further and finally push DeFi usage into the mainstream. 

However, before the combined application of these two technologies can truly cross the chasm into mass adoption, certain limitations and challenges must first be addressed. This article will examine how AI is impacting the current state of DeFi, the best use cases within it, and the immediate challenges they face.

How AI and DeFi Are Transforming Finance

Fraud Detection and Security Enhancements

Fraudulent activity remains one of DeFi's biggest ongoing problems. Wash trading, rug pulls, and pump-and-dump scams have run rampant across the industry since its inception. While they represent part of the growing pains of innovation in open-source technology, they can often cast a long shadow over the industry as a whole and cast doubt on the benefits that DeFi creates. Chainaysis reported that In 2023 alone, hackers exploited over $1.1 billion in user tokens.

While whitelisting addresses and time-locking new transactions can help prevent fraudulent activity at the end user, they are not enough to combat the ever-evolving sophistication of cybersecurity breaches on digital asset platforms.

AI can help reduce fraud and hacking rates because AI algorithms can more efficiently analyze data, identify patterns, and detect anomalies across decentralized ecosystems. Uniswap has already started implementing AI to identify and block fraudulent transactions.

AI-driven Trading Algorithms

Automated trading bots have existed since before DeFi, offering traders the benefit of executing trades on their behalf once certain parameters have been met. Companies like Shrimpy, 3Commas, Cryptohopper, and centralized exchanges like Kucoin have offered these services since as early as 2014.

Most of these early trading bots' parameters must be manually set by the owner, primarily for stop-loss trading. AI-driven trading algorithms expand upon these earlier models by employing deep learning techniques with self-correcting AI models incorporating Natural Language Processing (NLP). Rather than simple stop-loss arbitrary parameters, these new deep learning AI-driven algorithms can self-correct in real time and incorporate market sentiment through NLP from news outlets, and adjust strategies based on their previous trading histories.

Improved User Interface

Improving the user interface may be one of the least technologically impressive aspects of AI's capabilities. Yet, it is the most important from an end-user perspective. 

DeFi has largely remained a niche aspect of finance, reserved for those technologically savvy enough to navigate its complex and intimidating interfaces. By simplifying the process and making it approachable through chatbots and virtual assistants, DeFi instantly becomes more accessible to the average user. These new Chatbots can provide information, write basic code, and create agents to execute cross-chain transactions. Combined with AI-driven DeFi trading algorithms like those offered on 3Commas, Cryptohopper, and Shrimpy, any user can instantly execute complex trading techniques.

OpenAI’s general-purpose Chatbot ChatGPT and Anthropic’s Claude can provide knowledge, write simple code, and offer insights into DeFi. However, if users truly want to leverage the power of these new AI Chatbots, they should look to protocols like ChainGPT and KAVAAI, which can write and deploy smart contracts and create NFTs while also providing algorithmic trading assistance.

The Limitations and Challenges AI Modeling Faces in DeFi

AI modeling is primarily driven by quality data sets with high integrity that accurately reflect the content they report. Unfortunately, most data across DeFi is siloed into competing ecosystems, and interoperability between chains remains a difficult hurdle to overcome. This creates fractured data sets that only give a snapshot of different areas within DeFi, making comprehensive AI protocols difficult to create.

Data Privacy and Security

Data privacy and security are other areas of concern for AI protocols that wish to deploy on DeFi. One of the main attractions for many users of DeFi is the privacy and anonymity that decentralized protocols offer. A preference for protocols with data that is verifiable but reduces the necessity for extensive KYC policies that exist within TradFi and CEXs. 

AI protocols need to maintain the quality and integrity of the data they are building on while respecting the data owners' legal boundaries. Historically, the trend has been that ‘companies must inform individuals about the types of data it gathers, how it will be used, and who will have access to it.” The way AI protocols are trained makes this difficult to enforce since they do not directly source individual user data for use cases but create models to predict consistent responses. The data the models are built upon can inadvertently expose sensitive user data when other users issue commands.

Scalability and Computational Demand

The blockchain community has witnessed firsthand the ferocity of traditional media coverage regarding energy usage from consensus mechanisms like proof-of-work. As intensive as PoW is on energy demand, the scale and scope of what is currently required to power the AI movement is a degree of magnitude larger than the entire crypto industry.

Reports from Wells Fargo and Morgan Stanley show that energy usage from AI demand will surge 550% by 2026, from 8TWh in 2024 to 52TWh, before rising another 1,150% to 652TWh by 2030. This pace of innovation and energy demand is putting immense pressure on the supply chain of GPUs and the available electrical supply across energy grids. Governments, energy producers, and chip manufacturers will need to adjust their current approaches to keep pace with AI, and the relationships between these industries will be strained if they cannot. 

Wrap Up

While introducing AI into DeFi may represent the next paradigm shift in finance, the hard reality is that AI protocols are still limited to the data they are trained on. The protocols that already have access to vast amounts of reliable ecosystem data and have deep war chests to compete in the hyper-competitive supply chain of microchips will likely emerge as the strongest AI companies.

Regulations, data security, and computational demand are only some of the hurdles that AI companies must navigate in the coming years. Yet, the application of AI and DeFi in predictive markets, personal finance, and fraud detection can potentially revolutionize the financial industry in ways we have never seen.