Prediction markets are good at one thing: aggregating opinions. They are still bad at another: agreeing on fair value.
Yala thinks that gap is the biggest unsolved problem in prediction markets—and with Yala 2.0, the team is building an AI-native system designed to close it.
Rather than launching another trading venue or forecasting dashboard, Yala is positioning itself as something more fundamental: an AI agent that continuously estimates fair probabilities for events across global prediction markets.
If prediction markets are where information gets priced, Yala wants to be the reference signal everyone prices against.
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The Missing Layer in Prediction Markets
Modern prediction markets like Polymarket or Kalshi no longer resemble betting sites. They function more like financial markets, where prices represent probabilities and traders compete to express better information.
But unlike traditional markets—options, futures, credit—prediction markets lack a shared valuation framework. There is no equivalent of Black–Scholes. No consistent “fair price” reference. No common probability baseline.
That creates a structural problem:
New users face massive information asymmetry
Pricing can drift due to sentiment or thin liquidity
Traders rely on intuition instead of calibrated models
In short, markets are efficient—but incomplete. Yala’s core thesis is simple: prediction markets need a neutral, high-accuracy fair-value signal. And that signal is best produced by AI.
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What Yala 2.0 Actually Is
Yala 2.0 is an AI-native fair-value agent—a system designed to ingest market data, news, sentiment, and behavior, and output a probability that represents the most rational price for “Yes” or “No.”
If the market is pricing an outcome at 60%, and Yala estimates fair value at 72%, that gap is actionable.
Fair value doesn’t mean certainty. It means better decisions over time.
From Signals to a Fair-Value Engine
Yala is rolling out in stages, each designed to expand how fair value is produced and used.
Early on, the focus is credibility. Yala’s first fair-value agent is in closed testing, with early probability signals shared publicly on X. These outputs aren’t positioned as predictions of record—they’re proof points showing calibration, consistency, and explainable reasoning. This phase establishes Yala’s core idea: a repeatable, rational probability signal.
Next comes the public agent. Users will be able to query Yala with simple inputs—market type, target condition, and time horizon—and receive a probability estimate representing fair value. Behind the scenes, the agent blends historical market data, news and event analysis, social sentiment, and smart-money behavior.
The system will also trade with limited capital in live markets, allowing its logic to be tested against real outcomes, not just backtests. At this stage, Yala becomes a measurable pricing agent, not just an analytics tool.
Longer term, Yala scales into a multi-agent system. Instead of a single output, specialized agents evaluate different dimensions of uncertainty—markets, sentiment, macro events, options data, and simulations.
The result isn’t just one number, but probability distributions, confidence ranges, and scenario analysis across domains like crypto, equities, elections, sports, and macro policy. This is where Yala starts to function as fair-value infrastructure rather than a standalone product.
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Why It Matters
Prediction markets work best when participants share a reliable reference point. Fair value gives traders clearer decisions, liquidity providers better risk pricing, and markets a faster path to truth. It also allows AI agents to coordinate using a common probabilistic language.
Yala’s bet is simple: the next evolution of prediction markets won’t come from launching more markets—it will come from better pricing intelligence.
Where the YALA Token Fits
As usage grows, the YALA token underpins governance and economic alignment. Stakers help oversee agent behavior, parameter updates, and system evolution, while revenue from agent usage is designed to flow back into the ecosystem. As new agents and modules are introduced, long-term YALA holders capture upside tied to real adoption—not speculation.
