The nation is well-positioned to further develop artificial intelligence (AI) and promote its application, while the government renews its interest in cryptocurrency (crypto) as a significant part of digital assets. Both AI application and crypto hold huge potential to advance collective prosperity, yet they are rooted in complex, disruptive technologies that pose significant challenges for policymakers.
Traditionally, the two fields have followed separate trajectories, but their convergence is increasingly evident. From the perspective of crypto, AI and specifically machine learning (ML) can be deployed to enhance processes that involve intensive computation and advanced decision-making. For example, during blockchain operations, user-initiated transactions are pooled so that miners can select transactions and solve intricate cryptographic puzzles to add new blocks. This mining process is the most compute-intensive aspect of the blockchain, yet it is essential for maintaining the chain’s integrity. ML can offer multi-faceted support, from selecting optimal mining strategies and assigning mining tasks efficiently to predicting faulty conditions in mining equipment.
ML also holds promise for improving crypto trading, which, unlike conventional securities trading, is characterized by around-the-clock activity and extreme volatility. Advanced ML systems are capable of operating continuously with fault tolerance. They excel at filtering out noise, capturing subtle patterns, and adapting in real time to chaotic and rapidly changing conditions. These attributes make ML especially well-suited for predicting prices, detecting fraudulent activities, and optimizing portfolios based on numerous technical, psychological, and environmental factors.
Conversely, from the perspective of AI, crypto and specifically blockchain could be used to manage data, model training, and related transactions. Modern, high-end AI models are trained on an enormous amount of data, which requires extensive storage and processing power. Today, such an AI model is generally built by a single company, even if the training data originates from many different sources. This often creates conflicts with the data owners and limits AI model access to paying customers. A blockchain-based approach could unite many stakeholders, not only addressing rights of data owners, but also enabling crowd sourcing for model training, which in turn could make AI models more accessible to a broader audience.
In general, blockchain can adopt several approaches to keep training data from different sources secret while also distributing AI model training to different parties. Building on this foundation, blockchain technology can seamlessly support transactions related to training data, AI models, and model outputs in terms of accountability and reward, access control and usage management, and transparency and auditability. With its immutable ledger, blockchain ensures transaction records remain both accessible and secure. Blockchain tokens incentivize active contribution of content and knowledge. Furthermore, smart contracts, widely used within blockchain systems, streamline the enforcement of governance rules.
Despite the promising potential of AI application and crypto, each field poses serious challenges for policymakers. These complex underlying technologies produce nuanced yet potentially profound impacts, making it imperative to thoroughly understand their inner workings and assess their societal implications. Regulating either AI application or crypto is already a formidable task, which explains the slow and deliberate pace at which regulations are developed and implemented. Their convergence only multiplies the difficulty, especially given the involvement of diverse stakeholders with often conflicting interests.
A sensible strategy for managing the convergence of AI and crypto is to take a risk-based approach—identifying and mitigating potential risks before fully leveraging their combined benefits. Currently, the major risks associated with AI application and crypto include misinformation, discrimination, and financial loss. Remarkably, the integration of these two fields can help alleviate some of these concerns: applying ML to crypto trading can enhance decision-making and promote economic stability, while leveraging blockchain in AI implementation can foster collaboration, leading to more representative data inputs and higher accuracy in outputs.
The increasing momentum in policymaking is encouraging. While regulatory activities have traditionally been the focus, when complemented by robust educational initiatives, they have the potential to avert a future where the dominant risks undermine our collective ability to think critically, trust our instincts, and make sound judgments. Moreover, by integrating forward-thinking regulations with targeted, practical education programs, we can concretely protect our communities while empowering individuals to harness emerging technologies for positive change. This combined approach not only lowers risk exposure but also cultivates a resilient environment in which innovation thrives for the benefit of all.
