Yala Enters a New Chapter in Prediction Markets

Today marks the beginning of a new phase for Yala, focused on solving a core limitation in prediction markets: the absence of a reliable, accessible fair-value signal. Yala is evolving into an AI-native fair-value agent designed to improve predictive accuracy and democratize advanced probabilistic tools for all market participants.

Building an AI-Native Fair-Value Engine

Yala’s roadmap outlines its transformation into a scalable fair-value engine powered by modular AI components and data-driven probability models. The long-term vision is to expand across markets, domains, and application scenarios, positioning Yala as foundational infrastructure for global prediction markets.

Why Prediction Markets Need Fair Value

Prediction markets have proven highly efficient at aggregating information, yet they remain incomplete. They lack a systematic, high-accuracy fair-value reference, which leads to information asymmetry and inconsistent pricing. While markets excel at reflecting collective sentiment, they do not inherently provide a rational benchmark for what probabilities should be.

From Elections to Financial Infrastructure

The 2024 US presidential election highlighted this gap. While traditional polling showed a statistical tie, prediction markets consistently priced a different outcome. This ability to surface real-time collective intelligence has pushed prediction markets beyond gambling into recognized financial infrastructure, as confirmed by Kalshi’s approval by the CFTC as a Designated Contract Market. Prices are now set through order-book matching, where probabilities are negotiated rather than imposed.

The Missing Equivalent of Black–Scholes

Prediction markets increasingly resemble options markets, yet they lack an equivalent to fair-value pricing models such as Black–Scholes. Without a robust fair-value framework, prediction markets cannot fully mature into serious financial instruments. For traders, fair value acts as a statistical north star, identifying opportunities where market prices diverge from rational probability.

Why AI Is Essential for Fair Value

Calculating fair value in prediction markets is inherently complex. Outcomes depend on countless interacting variables that exceed human cognitive limits. Unlike options pricing, there is no single closed-form equation. AI agents are uniquely suited to this task, as they can integrate diverse signals, adapt dynamically, and output calibrated probability estimates that function as fair prices.

How Fair Value Guides Rational Decisions

When an AI-derived fair value exceeds the market price of “Yes,” buying Yes or selling No becomes statistically favorable. When fair value falls below the market price, selling Yes or buying No is the more rational choice. While fair value does not guarantee perfect predictions, it systematically improves decision quality and long-term outcomes, transforming prediction markets from speculation into structured information-pricing systems.

Yala’s Early Stage: Establishing the First Agent

In the early phase, Yala focuses on closed testing of its first fair-value AI agent while publishing early probability outputs through its official X account. This stage emphasizes calibration, consistency, and probabilistic reasoning, laying the methodological foundation for more advanced capabilities.

Yala’s Mid Stage: Public Launch and Live Validation

As development matures, Yala transitions to the public launch of its fair-value AI agent. The model is purpose-built for prediction markets and risk-neutral valuation, with performance continuously evaluated in live conditions. The agent primarily leverages historical trading data while incorporating news analysis, smart-money signals, and social sentiment to refine its estimates.

How Users Interact With the Agent

Users provide structured inputs defining the market type, target condition, and time horizon. The agent responds with a probability estimate representing fair value, which serves as a reference point for directional or range-based trading decisions.

Live Trading and Modular Architecture

During this stage, the agent operates autonomously in a controlled live environment, managing limited real capital to validate its logic under market conditions. The system is built on a modular multi-agent architecture coordinated by a central orchestrator, enabling rapid adaptation, plug-and-play expansion, and seamless support for future Yala components.

The Long-Term Vision for Yala

Yala is ultimately building toward a multi-agent swarm system capable of cross-domain fair-value evaluation, subjective pricing, private-information adjustment, and tokenized agent economies. The goal is a future where AI-driven fair-value agents form the probabilistic backbone of global prediction markets, enabling markets, agents, and users to coordinate around accurate and verifiable probability signals.