Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
The frustration is familiar to anyone who uses artificial intelligence (AI) to research investments. ChatGPT or Claude builds the thesis, weighs the risks and names a position. Then the tab closes and the brokerage app opens. Analysis and execution have always lived in separate places — until now.
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Liquid’s new Co-Invest app is embedded directly inside ChatGPT and Claude, letting users fund accounts, analyze markets and place live trades without leaving the conversation. According to a Tuesday (May 26) press release, the app covers more than 500 markets spanning crypto, equities, foreign exchange, Polymarket prediction market positions and pre-IPO secondaries. Users can set stop-loss and take-profit levels inside the chat and confirm each order with a tap. Liquid operates non-custodially, keeping user funds in their own wallets.
Millions of people already listen to AI to figure out what to invest in, Franklyn Wang, Liquid’s CEO, told The Block. Putting execution in the same interface, he said, is the next logical step.
The market Co-Invest is entering is already large. PYMNTS Intelligence found that as of December, 37% of power AI users reported using native AI platforms as their primary tool for managing finances and banking. Among mainstream users, the share doubled in a single month. PYMNTS Intelligence also reported that only 14% of light users felt comfortable using AI for financial tasks, a gap that points to how concentrated financial AI adoption remains among the most engaged users.
Co-Invest arrives alongside a cluster of similar moves. Robinhood launched Agentic Trading and an Agentic Credit Card, letting AI agents execute trades and card payments on a customer’s behalf through a dedicated virtual card with a spending limit the user sets. MoonPay recently launched its own ChatGPT integration for crypto purchases. OpenAI rolled out personal finance tools for ChatGPT Pro users through a Plaid integration in May. Gemini introduced agentic trading that lets AI models connect directly to exchange accounts. The pattern across all of them is the same: AI assistants are moving from information layer to transaction layer.
The convenience argument is straightforward. The risk argument is harder to dismiss. FINRA’s 2026 Annual Regulatory Oversight Report flagged hallucinations as a specific compliance concern for broker-dealers, warning firms to develop procedures for AI agents that may act beyond the user’s intended scope. The regulator defined hallucinations as instances where a model generates information that is inaccurate or misleading yet presents it as factual.
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In financial contexts that definition carries direct consequences. Fortune reported in April that large language models are inherently stochastic, meaning even well-tuned agents can produce errors with the same confidence as correct outputs. When an agent sits on top of a brokerage account, a hallucinated price, a misread filing or a misinterpreted order parameter can move money before a user notices.
Both Liquid and Robinhood built confirmation steps into their products. Liquid requires a user tap to confirm every trade. Robinhood lets customers choose whether to require manual approvals and restricts agents by default to a dedicated virtual card with no access to primary account information. Robinhood Vice President of Product Management Abhishek Fatehpuria said that the design reflects what customers said they wanted: to give agents the power of Robinhood, but in a safe way.
Whether those guardrails are sufficient for users following AI-generated analysis that may be confidently wrong is a question that has yet to be answered.
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