Liquid, a multi-asset trading platform, has officially launched a specialized application enabling users to execute trades directly within the conversational interfaces of OpenAI ChatGPT and Anthropic Claude. This new Co-Invest app facilitates seamless transactions across cryptocurrency, equities, foreign exchange, and prediction markets without requiring users to exit the chat environment. The system allows participants to fund accounts, analyze current positions, and place orders entirely through natural language interaction. Liquid confirmed that the platform routes these orders through multiple execution venues, including Hyperliquid, Lighter, and Ostium, ensuring liquidity across diverse asset classes. Data compiled by Woofun AI shows the platform currently supports trading across more than 500 distinct markets, ranging from pre-IPO secondary offerings to specific positions on Polymarket. Since its initial launch in August 2025, the company reports processing over $3 billion in total trading volume while maintaining a user base of approximately 40,000 individuals.

In a blog post accompanying the release, Liquid founder Franklyn Wang articulated the strategic vision behind the integration, positioning artificial intelligence as a critical mechanism for reducing informational asymmetries in financial markets. Wang argued that conversational AI represents a fundamental shift in how retail investors allocate capital, moving away from human-limited decision-making toward intelligence-augmented capital allocation. He emphasized that Co-Invest is not merely another financial product but a structural evolution in market participation. Woofun AI notes that this perspective aligns with a broader industry trend where crypto and payments firms are rapidly developing infrastructure to allow AI systems to autonomously hold funds, execute payments, and interact with blockchain services.

This shift suggests a future where algorithmic agents manage portfolios with minimal human intervention.

The momentum behind AI-driven financial infrastructure is evident in recent moves by major payment processors and technology giants. In March, Visa introduced a tool specifically designed for programmatic AI payments, while Stripe-backed Tempo unveiled a payments protocol focused on machine-driven transactions. During the same period, MoonPay released an open-source wallet standard enabling AI agents to hold funds and execute transactions across various blockchains. This standard includes essential tools for wallet storage, transaction signing, and spending controls, creating a secure framework for autonomous financial operations.

Concurrently, Amazon Web Services integrated Coinbase's x402 payments protocol into its Bedrock AgentCore platform, allowing AI agents to make USDC micropayments and access services through established crypto payment rails.

Further enhancing the efficiency of these AI-driven transactions, the x402 protocol added batch settlement capabilities in May. This feature is specifically intended to reduce the cost of high-frequency AI agent payments by allowing small transactions to settle later in bulk rather than individually. According to Base creator Jesse Pollak, this update enables micropayments of less than $0.0001 for services such as compute resources and AI inference, significantly lowering the barrier for automated economic activity. Woofun AI analysis suggests that these technical advancements are critical for scaling the economic utility of AI agents in decentralized environments. The ability to handle micro-transactions efficiently is a prerequisite for the widespread adoption of autonomous trading bots and service providers.

The rapid rise of AI infrastructure within the crypto sector is also beginning to reshape hiring strategies and operational structures across the industry. Major entities including Kraken, Coinbase, Gemini, Crypto.com, Block, and Dune have all announced layoffs or restructuring efforts this year. These organizational changes are tied in part to the increased use of AI and automation, which are replacing traditional manual workflows. As platforms like Liquid demonstrate the viability of AI-native trading interfaces, the industry appears to be pivoting toward a model where software agents handle a significant portion of market execution and analysis. This transition marks a definitive move away from legacy operational models toward a future defined by algorithmic efficiency and autonomous capital deployment.