Beyond Polymarket: How DeAgent AI Is Emerging as the Value Hub of the Prediction Market

Since the dawn of human civilization, people have always been curious about the future — and willing to bet on it. In the crypto-native era, prediction markets are transforming this ancient instinct into a public good: measurable, tradable, and reusable.

 

 

In the past decade, the democratization of information was achieved by the Internet. In Web3, that same process is unfolding for value and belief, which are now being tokenized and priced — creating a more verifiable, incentive-aligned form of value democratization.

 

 

With the arrival of AI, prediction has evolved beyond simple price feeds into complex judgment and adjudication, turning prediction into a form of infrastructure rather than mere speculation.

 

 

In this view, prediction markets become a fundamental informational base layer for governance, hedging, and resource allocation.

 

 

In November 2025, Google began integrating Polymarket and Kalshi’s market probabilities into Google Finance, marking the first time prediction data entered a major public gateway with hundreds of millions of users. This is not only an endorsement of the sector — it’s a clear signal of growing mainstream demand.

 

 

Why Prediction Markets Are a Core Battleground for Web3

 

 

At its essence, a prediction market aggregates tacit knowledge dispersed among individuals into a public probability through pricing. This idea traces back to Robin Hanson’s concept of Futarchy— in which values are determined by voting, while facts are priced by markets.

 

 

In such a system, prediction markets serve as the primary mechanism for information aggregation. Academic research has long demonstrated that prediction markets often outperform traditional polling methods when it comes to forecasting real-world outcomes — particularly in their ability to dynamically update and align incentives among participants.

 

 

When we move from theory to real-world practice, this mechanism — turning distributed cognition into price signals — is now being validated by both capital and user adoption in 2024–2025.

 

 

Platforms like Polymarket and Kalshi have repeatedly seen daily trading volumes near or exceeding $100 million, with cumulative transaction volumes reaching tens of billions of dollars.

 

 

This marks the transition of prediction markets from niche experiments to full-scale market explosions. According to data, Polymarket reached a record 477,850 monthly active traders in October, surpassing its previous all-time high of 462,600 in January.

 

 

Its monthly trading value rebounded to an all-time high of $3.02 billion, up from a period between February and August when it hovered around or below $1 billion. The platform also saw 38,270 new markets created in October, nearly three times that of August.

 

 

In short, October set new records for Polymarket across all key indicators — trading volume, active traders, and newly created markets. Meanwhile, Kalshi even surpassed Polymarket in October, recording $4.4 billion in trading volume.

 

 

In parallel, as U.S. regulatory clarity improves and licensed entities are being acquired or restructured, the pathway for prediction markets to re-enter compliance within the U.S. is becoming increasingly clear.

 

 

Together, these developments indicate that the information-derivative market centered around prediction now demonstrates real, robust, and mainstream-recognized demand.

 

 

From a broader perspective, prediction markets function as general-purpose modules for risk hedging and governance. Corporations can hedge operational risks by trading on the probability of policy outcomes; DAOs can bind proposals and KPIs to conditional markets; media and data platforms can use probabilistic narratives as new layers of information display.

 

 

As major information gateways like Google and Perplexity AI integrate prediction data into their ecosystems, the age of “probability as an interface” is rapidly approaching.

 

 

The Investor’s Dilemma in a Booming Sector: Usable, but Not Investable

 

 

When a new sector enters its early stage of explosive growth, retail investors usually ask two fundamental questions: Is the demand real? And how can we share in the upside?

 

 

The first question has already been answered — the demand for prediction markets is clearly real and growing. But the second question reveals a persistent paradox: in this sector, the leading products are usable but not investable.

 

 

Take Polymarket as an example. For a long time, the project publicly stated that it had no native token and no announced plans for an airdrop or token generation event (TGE).

 

 

Although Polymarket’s CMO Matthew Modabber recently confirmed that a POLY token and an airdrop are indeed planned — and founder Shayne Coplan hinted at the same earlier in October — this still means that the most lucrative, asymmetric early-stage rewards have largely been captured by those who were deeply involved from the beginning.

 

 

For everyone else, unless you actively participate in each individual event market, it’s difficult to gain beta-level exposure to the sector’s long-term growth. In other words, for investors seeking index-style exposure to the prediction market boom, viable on-chain assets remain extremely scarce.

 

 

More broadly, other regulated prediction platforms such as Kalshi also lack native crypto tokens. Meanwhile, smaller on-chain prediction tools either lack sufficient scale and network effects to represent the sector, or function merely as single-purpose utilities, making them unfit to carry sector-level value.

 

 

The result is a structural gap: demand is exploding at the application layer, but there are no investable assets at the capital layer.

 

 

From Pump.fun and Virtuals to Polymarket and DeAgent AI

 

 

Looking back at the Meme sector in 2024, one of the most iconic phenomena was the rise of Pump.fun — a platform that ignited an unprecedented wave of on-chain creation with its ultra-low entry barrier and standardized bonding-curve issuance mechanism.

 

 

During its early explosive phase, Pump.fun did not have a native token, meaning users could only benefit from the boom by participating directly in the creation and trading of individual meme tokens. Later, as the market matured, a new tokenized vehicle emerged to index the ecosystem’s overall growth — Virtuals (VIRTUAL).

 

 

VIRTUAL linked key activities within the ecosystem — creation, trading, and liquidity provision (LP) — to the platform’s token economy. Holding VIRTUAL became roughly equivalent to holding an index of the entire Agent/Meme ecosystem’s growth, thereby absorbing the premium and narrative momentum unleashed by Pump.fun’s success.

 

 

When Pump.fun eventually launched its native token PUMP in mid-to-late 2025, it came after the initial wave of explosive growth. The timing misalignment meant its value capture mechanism lagged behind the ecosystem’s early expansion.

 

 

History suggests a recurring pattern: when the application layer erupts before a viable index asset exists, the first infrastructure project that offers both a working product and a tradable token tends to outperform the sector average in the subsequent value re-rating phase.

 

 

Now, in the emerging prediction market sector, the same dynamic is playing out — and DeAgentAI is positioned as that kind of infrastructure-layer player. DeAgentAI is an AI-agent infrastructure that spans across Sui, BSC, and BTC ecosystems, enabling trustless, autonomous decision-making by artificial intelligence agents on-chain.

 

 

It aims to solve three fundamental challenges AI faces in distributed environments: identity verification, continuity assurance & consensus formation, and building a trustworthy AI agent ecosystem.

 

 

Within the context of prediction markets and DeFi, DeAgentAI is constructing a core infrastructure protocol built around AI oracles and a multi-agent execution network. On one side, it interfaces with real-world and on-chain data, standardizing complex judgments, decisions, and signal generation into verifiable oracle outputs.

 

 

On the other, it connects these outputs to trading, governance, and derivative design, effectively positioning itself as the informational and value hub of the entire sector.

 

 

For this reason, today’s dynamics in the prediction market space are mirroring the Pump.fun–Virtuals pattern. Polymarket represents the Pump.fun equivalent — the leading product with massive user adoption but lacking an investable token — while DeAgentAI (AIA) plays the role of Virtuals, serving as the value container for the entire ecosystem.

 

 

It not only provides the critical infrastructure missing from the prediction market stack — AI oracles and agent execution networks — but also offers a publicly tradable token (AIA) that serves as an index-like anchor for the sector’s growth.

 

 

This allows investors to indirectly share in the medium- to long-term expansion of the prediction market ecosystem through holding AIA.

 

 

How DeAgent AI Becomes the Value Container for the Prediction Track

 

 

At the core of DeAgent AI’s technical architecture lies a mission to solve three fundamental challenges faced by decentralized AI agents operating on-chain: continuity, identity, and consensus. Through a hybrid short-term and long-term memory state system, along with on-chain state snapshots, each AI Agent within DeAgentAI maintains persistence across multiple chains and tasks.

 

 

Its actions and decisions form a complete, traceable lifecycle — preventing the “reset” problem that typically occurs in stateless AI models.

 

 

DeAgentAI also employs unique on-chain identities (DIDs) and tiered authorization mechanisms, ensuring that each agent’s identity is verifiable and tamper-proof. For decision-making and consensus, it introduces a Minimum Entropy Decision (MED) mechanism, combined with validator consensus, to aggregate messy outputs from multiple models into a single, verifiable, and deterministic result that can be settled on-chain.

 

 

Building upon these foundations, DeAgentAI further integrates several modular protocols: The A2A (Agent-to-Agent) protocol, which standardizes collaboration between AI agents; The MPC (Multi-Party Computation) execution layer, which guarantees privacy and security for sensitive operations; Together, they unify identity, security, decision-making, and collaboration into a verifiable and scalable decentralized AI-agent infrastructure.

 

 

Dual-Layer Implementation: AlphaX and CorrAI

 

 

At the application layer, this infrastructure is materializing through two major products — AlphaX and CorrAI. AlphaX is the first AI model incubated by the DeAgentAI community. It integrates Transformer architecture, Mixture-of-Experts (MoE), and Reinforcement Learning from Human Feedback (RHF) to enhance the accuracy of cryptocurrency price predictions.

 

 

AlphaX focuses on forecasting crypto price trends over 2–72-hour horizons and has achieved a 72.3% accuracy rate. In simulated live trading during December 2024 and January 2025, it generated +18.21% and +16.00% ROI, respectively, with a 90% win rate, demonstrating the real-world viability of AI-driven prediction in active trading environments.

 

 

CorrAI, by contrast, functions as a no-code Copilot for DeFi and quantitative users. It helps them select strategy templates, fine-tune parameters, run backtests, and execute on-chain instructions — effectively linking signal recognition and strategy execution into a closed loop. This also funnels real capital and behavioral data into DeAgent AI’s broader agent network, strengthening its ecosystem.

 

 

On the ecosystem side, AlphaX has already built a substantial user base and on-chain activity through integrations and campaigns on Sui, BNB Chain, and other public blockchains. With support for multiple chains and diverse use cases, the DeAgent AI network has now generated hundreds of millions of on-chain interactions and tens of millions of unique user relationships. It is no longer a whitepaper concept, but a live, continuously utilized infrastructure.

 

 

From Price Feeds to Subjective Judgment: The AI Oracle

 

 

Traditional oracles mainly handle objective data feeds such as BTC/USD prices, relying on redundant nodes and aggregated data sources to reach consensus. However, once the question involves subjective or non-deterministic judgment — for example, “Is ETH more likely to rise or fall this weekend?” — traditional oracles struggle.

 

 

Each node might query a different large language model, leading to inconsistent outputs that cannot be independently verified. Moreover, it becomes nearly impossible to prove that a node truly called the model it claims to have used or obtained a specific result. Under these conditions, trust and security both collapse.

 

 

DeAgentAI was designed from the outset to address this gap with its DeAgentAI Oracle, built specifically for subjective prediction and reasoning tasks. Here’s how it works:

 

Users submit a question in a multiple-choice format, along with a service fee. Multiple AI Agents in the network independently perform retrieval and reasoning to reach their own judgments, and then vote on the outcome. The on-chain smart contract aggregates all votes, determines the final consensus result, and records it immutably on the blockchain.

 

 

As a result, what was once a divergent set of AI outputs becomes a deterministic, settleable on-chain result. Whether you “trust” any single AI model no longer matters — the process is now verifiable, transparent, and repeatable.

 

 

For the first time, AI decision-making itself becomes an on-chain public service, perfectly suited for use cases like prediction markets, governance adjudication, and InfoFi (information-finance) scenarios. This component is currently undergoing internal testing within the DeAgentAI network.

 

 

In practical testing, DeAgentAI’s agents have already been deployed to make judgments around real-world events. For example, during the recent U.S. federal government shutdown, the team used market data from Kalshi and Polymarket, combined with historical shutdown durations, bipartisan negotiation patterns, and key calendar milestones to build a decision-tree model and ultimately predicted that the shutdown was most likely to end between November 12–15 (or within the adjacent November 13–20 window) — a forecast notably more structured and grounded than the endless “indefinite stalemate” narratives circulating in the market at the time.

 

 

In another case, when facing the ongoing debate over “Has Bitcoin entered a bear market?”, DeAgent AI integrated on-chain metrics, ETF capital flows, macro policy shifts, and technical indicator divergences. The system concluded that Bitcoin was in a deep correction phase typical of early bear cycles, rather than the continuation of an accelerating bull run. It also provided key price levels and risk-monitoring frameworks accordingly.

 

 

These examples illustrate two things: DeAgentAI Oracle’s capability to decompose and aggregate complex, subjective problems into coherent, verifiable predictions; and its outputs are already directly applicable to prediction markets and trading decision-making — far beyond the level of a conceptual demo.

 

 

How AIA Indexes the Growth of the Prediction Market Sector

 

 

 

From an investor’s perspective, the value-capture mechanism of AIA lies in its dual functionality — it is both the medium of payment and settlement within the DeAgentAI Oracle and Agent network, and the staking and governance asset for validators and nodes.

 

 

As more prediction applications, governance modules, and DeFi strategies integrate with this network, the number of requests, calls, and security requirements will translate directly into real demand for AIA. This naturally ties the token’s value to the actual usage volume of the ecosystem, rather than to speculative or one-off narrative hype. Crucially, this value chain is circular and traceable.

 

When platforms like Polymarket expand into more complex or subjective prediction categories, they will inevitably need to rely on AI oracles to handle nuanced judgments. Each such oracle call drives increased demand for infrastructure providers like DeAgentAI.

 

 

As usage of the Oracle/Agent network scales, the AIA token — used for payment, settlement, and staking — experiences proportional demand growth. In other words, if you believe the prediction market will continue to grow, it logically follows that demand for AI-powered oracles will expand as well — and this correlation will ultimately be reflected in AIA’s long-term valuation.

 

 

From an asset classification standpoint, AIA uniquely satisfies both functional and investable criteria. On one hand, AIA underpins the AI oracle and agent infrastructure that powers subjective, probabilistic prediction — addressing a core bottleneck in the prediction market stack. On the other hand, it is a publicly tradable token, allowing investors to obtain direct exposure to the sector’s infrastructure growth.

 

 

By contrast, regulated platforms such as Kalshi and Polymarket still lack native tokens; while traditional price-oracle tokens (e.g., Chainlink) serve the objective data-feed market, they do not participate in the AI-driven subjective oracle economy.

 

 

In this sense, AIA currently stands out as one of the very few — perhaps the only — assets that are both usable and investable within the AI oracle space.

 

 

This makes AIA a natural index vehicle for the growth of the prediction market sector, capturing the upside of an expanding ecosystem while anchoring its value in genuine on-chain utility.

 

 

How to Participate in the Prediction Track?

 

 

At this stage, the prediction market sector has clearly entered a phase where applications take the spotlight, while value is quietly consolidating underneath. Platforms like Polymarket and Kalshi have already proven that real transactional demand exists.

 

 

Yet, what may ultimately sustain long-term valuation is not just these applications themselves, but the infrastructure layer that supports their judgment and settlement processes — namely, AI oracles, agent networks, and their associated functional tokens.

 

 

As prediction platforms attempt to handle more complex and subjective forms of judgment, they will inevitably place greater and more frequent demands on AI oracle infrastructure.

 

 

These calls will translate into the continuous use of networks like DeAgent AI, and the functional tokens tied to payment, settlement, and staking within those systems will naturally absorb part of that value flow. Therefore, the key question is no longer whether to participate in this sector — but rather how and at what level to participate.

 

 

A rational approach would be a two-layer strategy: At the application layer, participate with engagement. Use platforms like Polymarket to capture short-term alpha by betting on specific events or narratives.

 

 

At the infrastructure layer, participate with positioning. Allocate exposure to AIA as a way to align with the long-term thesis that AI oracles will become standard infrastructure for prediction markets. The first layer answers the question: “Can I make money this round?” The second layer answers the deeper one: “When the entire sector expands, am I positioned to rise with its foundation?”

 

 

Of course, AIA is just one factor within a diversified portfolio — not a substitute for sound risk management. The more prudent approach is to treat it as a sector infrastructure index component, allocating a reasonable share of your risk budget to this long-term thesis and letting the market validate your conviction over time.

 

 

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