Elastics raises €1.7M pre-seed for AI agents targeting prediction-market traders
The AMW Read
Novelty 2: Elastics is a new entrant applying agentic abstraction to prediction-market tooling, updating the player map in Finance/Ops. Significance 2: The thesis—AI replicating hedge-fund quant infrastructure for retail—could reshape retail brokerage if proven, affecting segment-level distribution
Elastics raises €1.7M pre-seed for AI agents targeting prediction-market traders
Warsaw-based Elastics has raised €1.7 million in oversubscribed pre-seed funding to build an AI-native operating system for prediction markets. Founded in April 2025 by Szymon Pawica (ex-Goldman Sachs) and Mateusz Brodowicz (quantitative modeler), the platform lets retail traders describe strategies in plain language and have AI agents handle signal generation, execution, and portfolio management. The round was led by French VC Frst, with participation including ElevenLabs co-founders Mati Staniszewski and Piotr Dabkowski, among other angels.
Why it matters: This is a direct challenge to the institutional data-and-infrastructure moat that has long separated quantitative hedge funds from retail traders. Prediction markets such as Polymarket ($9B valuation, backed by ICE) and Kalshi ($22B valuation) are growing fast, but the tooling available to individual participants remains primitive—essentially the same market/limit order interface that predates modern AI. Elastics exemplifies the "fastest-ARR-ramp via agentic abstraction" pattern: by wrapping quantitative workflows in a conversational LLM interface, it could unlock a new category of retail quant trading. The news also updates the debate over whether AI agents will primarily automate enterprise back-office tasks or reshape consumer-facing financial markets. If Elastics succeeds, prediction-market trading becomes another test case for the thesis that LLM-native interfaces cannibalize traditional fintech UX.
Expert take: The company's edge lies not in novel model architecture but in domain-specific agent orchestration. Pawica's insight—that hedge funds' advantage is people and infrastructure, which AI can now replicate for everyone—rests on the assumption that frontier LLMs have absorbed enough financial reasoning to make "trade with words" reliable. That assumption remains unproven at scale; prediction markets are adversarial, low-liquidity environments where even small execution errors can be costly. The bet is that inference cost continues to drop and agent reliability crosses a threshold where the convenience of conversational trading outweighs the precision of spreadsheet-driven strategies. If the thesis holds, Elastics won't just be a tool for Polymarket day-traders—it will be a template for AI-native retail brokerage more broadly.