The tool turns plain-English strategies into autonomous execution on Polymarket and Kalshi, opening systematic trading to a broader cohort and potentially reshaping prediction market dynamics.
Moonpay disclosed Monday that it acquired Dawn Labs and simultaneously launched Dawn CLI, an artificial intelligence-native trading tool that converts plain English strategy descriptions into autonomous trade execution on supported venues. The acquisition targets the growing base of active traders on prediction markets such as Polymarket and Kalshi, a corner of the crypto market where infrastructure has been fragmented and manually intensive.
Building a trading strategy for these venues has traditionally required skills across research, software development, and portfolio management. Dawn CLI is designed to collapse that multi-disciplinary stack into a single interface. A user describes a strategy in plain English; the system surfaces relevant data, generates and stress-tests code, and executes trades continuously based on the user’s direction.
The tool manages a four-stage process. First, a trader describes a strategy, such as “buy yes shares on a Polymarket contract when the implied probability diverges from a polling average.” Second, the platform pulls in data and market signals for evaluation. Third, strategy code is generated and backtested. Fourth, trades are executed autonomously on the supported venues.
“The team at Dawn Labs have made the most complex parts of active trading accessible to anyone with an idea,” said Ivan Soto-Wright, CEO and Founder of Moonpay. “With Dawn, traders can direct AI agents to develop and execute sophisticated trading strategies autonomously.”
Dawn Labs was founded in 2025 by Neeraj Prasad, a computer science and engineering graduate of MIT who conducted machine learning research in the university’s neuroscience labs. Before starting Dawn Labs, Prasad held engineering roles at Waymo, Microsoft, Citadel, and Reservoir Labs, spanning perception systems, machine learning infrastructure, quantitative trading, and deep learning compilers. The acquisition brings Prasad and the full Dawn team into Moonpay. He will serve as Chief Engineer of Moonpay Labs.
The simple read on this announcement is that Moonpay is adding an AI chatbot for trading. That interpretation misses the structural shift the product represents. The better market read focuses on who gains access and what that does to market dynamics.
Prediction market trading has been dominated by participants who can build and maintain their own infrastructure: quantitative funds, technically skilled individuals, and teams with dedicated developers. Dawn CLI lowers the barrier far enough that a trader with deep domain expertise but no coding ability can deploy a systematic strategy. The tool does not simply automate existing workflows; it makes strategy creation accessible to a cohort that was previously locked out of automated execution.
That access shift has implications for liquidity and market efficiency. More participants deploying systematic strategies can narrow spreads and increase volume on venues like Polymarket and Kalshi. At the same time, it raises the competitive bar. Strategies that once worked because few could execute them may become crowded faster. The tool’s eventual impact will depend on how well it handles the messy reality of live markets, where execution quality, latency, and venue-specific quirks determine profitability.
Prasad described the problem Dawn was built to solve. “Competing effectively has required expertise across multiple disciplines at once,” he said. “Dawn brings those capabilities into a single autonomous system. Joining Moonpay allows us to scale that system to a broader audience.”
AI-native trading tools face a credibility problem. Many products promise autonomous execution; few deliver it reliably when market conditions change. The difference between a demo and a live trading environment is the difference between a backtest and a real P&L.
Dawn CLI’s value proposition rests on its ability to handle strategy execution in live market conditions. The tool generates code, stress-tests it, and then executes trades continuously. That final step, the execution layer, is where most automated systems break. Venue APIs change. Liquidity evaporates. Slippage eats into expected returns. A tool that cannot adapt to these conditions will produce strategies that look compelling in simulation and fail in production.
Moonpay has been building toward AI-native financial infrastructure over the past year. The progression moved from on-ramp APIs to Moonpay CLI, then to Moonpay Agents with Ledger-secured hardware signing, and then to Moon Agents Card, a virtual Mastercard debit card that allows users and AI agents to spend stablecoins directly from on-chain balances anywhere Mastercard is accepted online. The company also launched the Open Wallet Standard, extending that infrastructure across agents, frameworks, and chains. Dawn CLI is the next step in that progression, and its success will be measured by how well it handles the execution layer.
Traders evaluating the tool should focus on three execution-specific questions:
The answers to those questions will separate a useful tool from a well-marketed prototype.
Dawn CLI’s adoption will not show up in a single headline metric. Traders tracking the tool’s impact should monitor a cluster of signals. Venue-specific volume changes on Polymarket and Kalshi may indicate strategy deployment. Publicly shared strategy templates or performance dashboards would signal user engagement. Integration announcements with additional prediction markets or decentralized exchanges would confirm Moonpay’s commitment to expanding the venue set.
The tool also creates a new vector for AI agent activity in crypto markets. Moonpay’s existing agent infrastructure, including Ledger-secured signing and the virtual debit card, means Dawn CLI strategies could eventually interact with on-chain protocols beyond prediction markets. That expansion path is not yet live. It is a logical next step given the company’s product trajectory.
For active traders, the immediate question is whether Dawn CLI reduces the time from idea to live strategy enough to justify switching from existing workflows. The plain English interface provides a differentiator only if the generated strategies perform in live conditions. Execution quality will determine whether Dawn CLI draws meaningful adoption from the prediction market crowd. The tool is now live. The market will deliver its verdict through usage, not announcements.
Drafted by the AlphaScala research model and grounded in primary market data – live prices, fundamentals, SEC filings, hedge-fund holdings, and insider activity. Each story is checked against AlphaScala publishing rules before release. Educational coverage, not personalized advice.