MoonPay acquired Dawn Labs and launched an AI trading copilot that turns plain-English prompts into strategies.
Crypto News
MoonPay has acquired Dawn Labs, an applied research startup building autonomous trading tools for prediction markets, and launched Dawn CLI, an AI-native product that converts plain-English instructions into automated trading strategies. The crypto payments company announced the acquisition on May 11 as part of a broader push into AI-powered financial infrastructure.
Dawn CLI allows users to describe a trading strategy in plain language. The system then generates the underlying code and executes the strategy, without requiring the user to have programming or quantitative finance skills. Neeraj Prasad, founder of Dawn Labs and now Chief Engineer of MoonPay Labs, said the product eliminates the need to be a developer, a quant, and a portfolio manager all at once. "Dawn collapses that into a single interface," Prasad said. "You describe what you want in plain English, and the system handles the code and execution."
Starting With Polymarket
The platform launches with initial support for Polymarket, a prediction market where users place bets on the outcomes of elections, sports events, economic indicators, and geopolitical developments. Prasad said prediction markets represent one of the fastest-growing sectors in crypto and that traders there are underserved by existing tooling. Expansion to additional trading venues and asset types is planned for the following months.
To reduce the risk of hallucinated strategies, unintended trades, or execution failures, Dawn CLI uses non-custodial wallets created locally through the Open Wallet Standard. The platform also provides reviewable strategy code that users can inspect before deployment, alongside policy controls that cap how much an agent can trade, which markets it can access, and how positions are sized.
MoonPay CEO Ivan Soto-Wright said the acquisition reflects what the company's next phase is about. "With Dawn, AI agents can develop and execute sophisticated trading strategies autonomously," Soto-Wright said. He described it as part of a company-wide model built around four pillars: fund, tokenize, trade, and spend.
AI Agents and Human Traders
Prasad said the technology is designed to serve both human traders and AI agents equally. "A human sets the strategy, and the agent executes it," he said. That framing contrasts with other platforms that treat AI agents and human users as separate customer bases requiring separate tooling.
