The Rise of AI-Powered Crypto: Unlocking the Future of Finance

The emergence of AI agents has marked a significant shift in the financial sector, with these autonomous systems moving beyond advisory roles to execute transactions. According to a recent PwC survey, 79% of companies are already adopting AI agents, demonstrating the rapid growth of this technology. This phenomenon has given rise to 'agentic finance,' a new paradigm where AI agents perform financial actions within predefined parameters set by humans. To understand agentic finance, it's essential to break it down into three layers: the agentic commerce layer, which focuses on discovery and decision-making; the agentic payments layer, which handles execution; and the asset management layer, which represents the full stack, enabling agents to manage portfolios and optimize financial strategies. While this may seem like granting full autonomy to AI agents, it's actually a form of conditional delegation, where users retain control through constraints. Theoretically, AI agents have a use case in the financial space, but they don't fit neatly into traditional financial infrastructure. This is where crypto comes into play, offering a financial layer better suited to machine-driven activity. Stablecoins provide AI agents with access to programmable, always-on money, while blockchains enable instant and global settlement. Crypto wallets, meanwhile, offer permissionless access to funds, forming a financial backend for autonomous systems. Early implementations of AI agents are already visible, with machine-to-machine payments and autonomous commerce allowing users to optimize retail research and get the best deals. In crypto-native environments, trading agents are widely deployed for portfolio management and trading strategies. On the enterprise side, supply chain management and vendor payments have been easily automated via AI agents, reducing errors and resource expenditure. Beyond these use cases, AI agents are driving new investable categories and demand for crypto. As AI agents can't operate on existing infrastructure rails, demand is growing for agent-native wallets, stablecoin payment rails, and data or compute marketplaces. For instance, Coinbase has launched an open payments protocol designed for agent-native transactions, marking a significant shift in the industry. Despite the momentum, there are risks and limitations associated with AI agents. Security is a primary concern, particularly around rogue or exploited agents executing unintended transactions. Regulatory clarity is still under scrutiny, and questions around authorization, liability, and treatment are being actively defined. To build trust for users, regulatory clarity from all stakeholders is necessary, enabling projects to build with confidence while safeguarding user funds and interests. Over the next twelve months, this technology is expected to grow and mature, with signals including growth in agent-driven transaction volume, emergence of agent-native wallets and payments protocols, and deeper integration between stablecoins and AI-driven systems. Regulatory clarity will heavily shape the pace and scope of adoption across different industries and fields. In conclusion, AI agents are not a theoretical concept; they are already executing transactions in limited environments. As the trend develops, crypto is increasingly emerging as the financial backend for machine-driven economies. For now, this is an infrastructure and long-term thematic play, but that is changing with rising adoption rates. Advisors should track it as a next-wave driver of crypto utility. A panel of AI experts was asked about the present and future of AI payments, revealing common themes and differences. The most mature use case is fraud detection, while emerging agentic payments enable autonomous AI to handle B2B treasury tasks and machine-to-machine micropayments using stablecoins. To scale AI payments, three gaps need to be closed: standardized agent identity, governance and explainability for regulators, and high-quality real-time data infrastructure.