Blockchain is increasingly presenting new opportunities for applications in combination with AI. In fact, Future Market Insights estimates that the global market for blockchain AI will skyrocket to $3.5 billion by 2032, with a 23% CAGR from 2023 to 2033 driven in part by small and medium enterprises adopting AI-based blockchain platforms to enhance smart contracts, payment systems and more.
“Reaching consensus on reality”
Paul Brody, Global Blockchain Leader at consulting powerhouse Ernst & Young (which has also become a blockchain powerhouse owing in large part to Paul’s thought leadership the last 7 years—including his early advocacy of Ethereum as the platform of choice for enterprise), has a characteristically nuanced take on this, telling Modern Consensus in an email:
“I don’t see AI and Crypto or blockchain converging into some combined concept so much as I see them being very complementary to each other. The way I look at modern computing infrastructure, there’s underlying data—facts and information about the world as it is—and then there is the ability to interpret that information. I see AI tools as just very advanced ways of interpreting data. The big problem that often gets overlooked is how bad the underlying data is. This is classic garbage in, garbage out. No matter who you are, human or machine, you make bad decisions if you have bad data. I see blockchains as these amazing tools for reaching consensus on reality—on who has what and where things are.”
A prominent advocate for public blockchains and privacy technology, Brody recently authored Ethereum for Business: A Plain-English Guide to the Use Cases that Generate Returns from Asset Management to Payments to Supply Chains. That title may sound like a mouthful, but the reality is that the ability to generate substantial returns is what has driven enterprise adoption of blockchain—and there are many examples of this adoption funding and furthering fundamental technological advances like zero-knowledge proofs.
In this second installment of Modern Consensus’ review of blockchain and AI, we’ll look at three use cases where AI has the potential to transform crypto to be widely accessible by making it more usable (see part one here: Crypto & AI: Competition, Convergence or Both? AI-based Trading).
AI-Driven Smart Contracts
Smart contracts are pieces of coding residing and executing on a blockchain that supports them—Ethereum being the first, as inventor Vitalik Buterin conceived the now second largest blockchain network by market capitalization as a way to create a general purpose blockchain (something Bitcoin was not designed to be) and bring to life a concept articulated by computer scientist Nick Szabo all the way back in 1994. The code executes automatically once predefined conditions are met. Unlike traditional contracts, smart contracts convert agreement statements into code, automatically enforcing accuracy, rules and penalties (if any are programmed in).
And perhaps most importantly, smart contracts do all this without a single, centralized authority, as is the case in the Web 2.0 paradigm. Not only can any node on the network verify the accuracy of the computation, public blockchains benefit from a large number of distributed nodes performing this verification process independently by running the chain’s common protocol, and once a sufficient number have done so the results become immutable, and forever available as a canonical reference point. Or as Brody puts it in the quote above, “reaching consensus on reality—on who has what and where things are.”
AI can add another layer of enhancement to smart contracts in the crypto ecosystem. Using predictive analysis, AI is enabling smart contracts to make informed decisions, adapt to market trends, and respond dynamically to changing conditions. Additionally, AI can facilitate risk assessment and hedging strategies, bolstering the reliability and benefits of smart contracts to risk-averse investors. Customization is another aspect, as the AI learns from user behavior and can tailor conditions accordingly.
Using AI & Blockchain To Detect Fraud
AI and blockchain may present an even more potent combination when deployed to combat fraud more effectively. Blockchain’s inherent security and transparency can complement AI’s ability to process vast amounts of information while detecting patterns and deviations imperceptible to humans. Additionally, the two technologies together can enable real-time fraud detection, providing a tamper-proof record of transactions and swift identification of suspicious activities.
Chainalysis, a blockchain analysis company founded in 2014, exemplifies this real-life use case by employing AI and machine learning algorithms to scrutinize transactional data on the blockchain. Rapidly detecting specific patterns, the company’s solutions provide immediate insights into possible dangers, earning trust from entities seeking compliance with Anti-Money Laundering (AML) regulations and Know Your Customer (KYC) processes. Crypto exchanges use Chainalysis for AML compliance reviews, while law enforcement authorities for years have been turning to it for investigating and successfully uncovering illicit activity. (See this related Modern Consensus story: Chainalysis asks: Did Bitcoin fund the Capitol riots?)
The company notes a customer base of more than 1,000 organizations, highlighting prominent financial services and technology companies including Barclays, BNY Mellon, Square—as well as, interestingly, the high-end audio and consumer electronics company Bang & Olufsen (this podcast explains their collaboration with artists and musicians on immersive experiences).
AI-Enhanced Crypto Portfolio Management
AI-powered asset management platforms are also helping consumers get more comfortable investing in crypto. As one example, One Click Crypto touts its ability to allow investors to diversify their portfolios, save on gas fees and generate better risk-reward yields. Founder Max Yampolsky explained to Modern Consensus that, “AI can offer personalised risk optimization, enabling users to maximize their yield while adhering to their individual risk profiles. [Our platform tailors] the portfolio of over 200 blue-chip yield farming pools and assets to align with each user’s risk appetite.”
AI’s ability to adapt rapidly to market shifts ensures portfolios can capitalize on opportunities and mitigate risks in real-time. Moreover, AI-powered holistic risk analysis considers various factors like macroeconomic indicators and geopolitical events, enabling investors to make well-informed decisions.
And for novice investors specifically, AI-powered platforms can be used to promote financial literacy by enhancing educational tools, such as with transparent explanations and easy to understand performance analytics. As AI technology evolves, sentiment analysis and natural language generation capabilities may be integrated to gauge market sentiment and simplify complex financial data.