How does DeAgent AI become the central hub of value in the prediction market sector outside of Polymarket?

Written by: ChandlerZ, Foresight News

If we say that human society has always been curious about and betting on the future, then crypto-native prediction markets are transforming this ancient demand into tradable, settled, and reusable public goods. Over the past decade, the democratization of information has been achieved through the internet; in the Web3 and crypto space, value and belief are also being tokenized and priced, forming a more verifiable and incentive-compatible democratization of value. The addition of AI pushes the boundaries of prediction from simple price feeds to more complex judgments and rulings, giving prediction a foundational infrastructure meaning—eliminating speculative interpretations. Prediction markets are increasingly becoming the underlying information infrastructure for governance, hedging, and resource allocation. Google’s integration of Polymarket and Kalshi market probabilities into Google Finance starting November 2025 marks the entry of prediction data into the public internet layer accessed by hundreds of millions of users, signaling both industry endorsement and growing demand.

Why prediction markets are a must-win in Web3

The essence of prediction markets is to aggregate dispersed tacit knowledge from individuals into a public probability through prices. This idea traces back to Robin Hanson’s Futarchy, where governance is decided by voting on value goals, while factual judgments are made through market pricing—making prediction markets the primary mechanism for information aggregation. Academic research also shows that prediction markets often outperform polls in depicting event outcomes, especially in dynamic updates and incentive constraints.

From a practical market perspective, this price-based knowledge aggregation mechanism is being validated by capital and users in 2024–2025. Prediction platforms like Polymarket and Kalshi often approach or surpass $100 million in daily trading volume, with total transaction volumes reaching hundreds of billions of dollars—marking a shift from niche experiments to full-scale explosion. Data shows Polymarket’s monthly active traders hit a record high of 477,850 in October, surpassing the previous January record of 462,600. Its monthly trading volume rebounded to a record $3.02 billion last month, after hovering around $1 billion from February to August. The platform opened 38,270 new markets in October—almost three times as many as in August. Polymarket’s trading volume, active traders, and new markets all hit historic highs. Meanwhile, Kalshi’s October trading volume even exceeded Polymarket’s, reaching $4.4 billion.

Additionally, with regulatory shifts in the US and the acquisition of regulated entities, the path for compliance and re-entry into the US market is becoming clearer. These developments collectively demonstrate that the core information derivatives market centered on prediction has genuine, strong, and mainstream-recognized demand.

From application spillover, prediction markets can be seen as a universal risk hedging and governance module. Companies can hedge operational risks based on policy implementation probabilities; DAOs can use conditional markets to tie proposals to KPIs; media and platforms can present probability narratives as a new layer of information display. The integration of information portals like Google and Perplexity with prediction platforms accelerates this era of probability-as-an-interface.

The investor dilemma amid a thriving track: usable but not investable

When a track experiences early explosive growth, ordinary investors usually ask two questions: one, is the demand real? and two, how can they share in the growth? We’ve seen the answer to the first; the second, however, remains a long-standing awkward reality in prediction, where top products are usable but not investable.

Take Polymarket as an example. Its official stance was once that the project had no native token and no announced airdrops or TGE plans. Recently, however, Polymarket’s Chief Marketing Officer Matthew Modabber confirmed plans for a POLY token and airdrops. Earlier in October, founder Shayne Coplan also hinted at launching POLY tokens. But this still means that for investors who did not participate deeply in early Polymarket markets, the most lucrative and asymmetric early redemptions have already been exhausted. Unless you actively participate in every market, it’s hard to gain beta exposure or long-term alignment with the track’s growth. For investors seeking exponential exposure to the track’s growth, such opportunities are extremely scarce.

More broadly, platforms like Kalshi, which offer regulated event contracts, also lack native tokens. On-chain prediction applications or tools either lack the scale and network effects to serve as industry indices or are more like single-function tools, unable to carry the entire track’s value attribution. As a result, application-layer demand is booming, but the investment layer faces a structural gap—no tokens to invest in.

From Pump.fun and Virtuals, to Polymarket and DeAgent AI

Reviewing the Meme track of 2024, one of the most representative phenomena is Pump.fun’s breakout. Its ultra-low barrier, standardized curve issuance mechanism ignited on-chain creation from zero to one. During its early surge, the platform had no native token; users could only share prosperity through individual meme bets. Later, a tokenized index of this ecosystem’s hype emerged—Virtuals (VIRTUAL). VIRTUAL ties key paths like creation, trading, and LP pairing within the ecosystem to the platform’s token, making holding VIRTUAL akin to holding an index of the entire Agent/Meme ecosystem’s growth, thus capturing the premium generated by Pump.fun’s narrative and fundamentals.

In mid-2025, Pump.fun launched its platform token PUMP, but the timing was later, and its value capture logic was misaligned with the earlier ecosystem explosion. Historical experience suggests that when application layers explode first without index assets, infrastructure projects that provide both products and tokens early tend to outperform the average in valuation reappraisal.

Returning to the emerging prediction market track, DeAgent AI plays such an infrastructure role. DeAgent AI is an AI agent infrastructure covering ecosystems like Sui, BSC, and BTC, enabling AI agents to make trustless autonomous decisions on-chain. It aims to solve three major challenges faced by AI in distributed environments: identity authentication, persistence, and consensus, building a trustworthy AI agent ecosystem.

Centered around prediction markets and DeFi scenarios, DeAgent AI constructs a foundational protocol with AI oracle and multi-agent execution network at its core. It connects real-world and on-chain data, standardizing complex judgments, rulings, and signals into verifiable oracle outputs. These outputs are then integrated into trading, governance, and derivatives via the agent network, making it the information and value hub of the entire track.

Because of this, the current prediction market scene is replaying this pattern. Polymarket corresponds to Pump.fun—product leader but lacking investable tokens long-term—while DeAgent AI (AIA) acts as a value container similar to Virtuals. It provides the missing key infrastructure modules—AI oracle and agent execution network—and offers a publicly tradable token AIA as a track index anchor, allowing investors to indirectly share the entire prediction market’s medium- and long-term growth through holding AIA.

How DeAgent AI becomes a value container for the prediction track

DeAgent AI’s core technical framework addresses three fundamental challenges for decentralized AI agents on-chain: continuity, identity, and consensus. It combines hot and long-term memory states, along with on-chain snapshots, so agents maintain a complete, traceable lifecycle across multi-chain and multi-task environments; uses a unique on-chain identity + DID and layered authorization to prevent identity forgery; and employs Minimum Entropy Decision and validator consensus to converge chaotic multi-model outputs into verifiable, settled results. Based on this, the A2A protocol standardizes agent collaboration, MPC execution layer ensures privacy and security of sensitive operations, and ultimately, the system integrates identity, security, decision-making, and collaboration into a scalable, verifiable decentralized AI agent infrastructure.

AlphaX and CorrAI: dual implementations of this infrastructure

On the application layer, AlphaX and CorrAI are the most direct realizations of this infrastructure. AlphaX is the first community-developed AI model based on DeAgentAI’s feedback training mechanism, utilizing Transformer architecture, Mixture-of-Experts (MoE), and Reinforcement Learning from Human Feedback (RHF), focused on improving crypto price prediction accuracy. AlphaX predicts 2–72 hour crypto price trends with 72.3% accuracy, achieving +18.21% and +16.00% ROI in live simulations in December 2024 and January 2025, with around 90% win rate—demonstrating practical AI prediction viability in real trading environments.

CorrAI functions as a no-code copilot for DeFi/quant traders, helping users select strategy templates, adjust parameters, backtest, and execute on-chain commands—closing the loop between signals and strategy execution, and bringing more real capital and activity into the DeAgent AI agent network.

Ecologically, AlphaX has accumulated a significant user base and interactions across public chains like Sui and BNB through activities and integrations. Coupled with multi-chain and multi-application scenarios, the DeAgent AI network now involves hundreds of millions of on-chain interactions and tens of millions of users—no longer a whitepaper experiment but a real, ongoing infrastructure.

From price feeds to subjective judgments: AI prediction oracle

Traditional oracles mainly handle objective data like BTC/USD, relying on multi-node redundancy and data source aggregation for consensus. But when the problem becomes subjective or non-deterministic—e.g., “Is ETH more likely to rise or fall this weekend?”—nodes call large models independently, often producing inconsistent answers, making it hard to verify whether a specific model was invoked or if the answer is trustworthy. Security and trust break down.

DeAgent AI’s solution is the DeAgentAI Oracle, designed for such subjective questions. Users submit multiple-choice questions and pay a fee; multiple AI agents retrieve, reason, and vote independently; a smart contract aggregates votes, determines the final result, and records it on-chain. This compresses divergent AI outputs into a verifiable, settled result, transforming AI judgment into a public service that can be repeatedly called on-chain—ideal for prediction markets, governance, and InfoFi. This component is currently in internal testing.

For example, during the recent US government shutdown, DeAgent AI used market prices from Kalshi and Polymarket, combined with historical shutdown durations, partisan dynamics, and key dates, to build a decision tree model concluding that the shutdown was most likely to end around November 12–15 (or close to November 13–20), rather than an indefinite stalemate as often portrayed.

Similarly, on the contentious topic “Has Bitcoin entered a bear market?” DeAgent AI integrated on-chain data, ETF flows, macro policy shifts, and technical divergence signals, concluding that the current phase is closer to a “deep correction early in a bear market” rather than an ongoing accelerated bull run, providing key price levels and risk monitoring frameworks.

These topic-specific predictions demonstrate DeAgent AI’s ability to decompose and synthesize subjective, complex issues, and show that its outputs can directly inform prediction markets and trading decisions, not just as demos.

How AIA indexes the track’s growth

From an investor’s perspective, AIA’s value capture logic is that it serves both as a payment and settlement medium for DeAgentAI Oracle and agent network, and as a staking and governance token for nodes and validators. As more prediction applications, governance modules, and DeFi strategies integrate with this network, request frequency, usage, and security demands will translate into actual demand for AIA, aligning its value with the overall track’s usage.

This value chain is self-reinforcing and forward-looking. As prediction applications expand into more complex and subjective questions, they will increasingly rely on AI oracles for judgment. This demand will drive continued use of infrastructure like DeAgent AI. The functional tokens (AIA) used for payments, settlements, and staking will then see their demand and value rise accordingly. If one believes prediction markets will continue to grow, it’s hard not to believe that demand for AI prediction oracles will also expand, ultimately reflected in AIA’s long-term valuation.

Asset attributes-wise, AIA combines “functionality” and “investability.” It is a tokenized infrastructure for subjective AI oracle and agent services—addressing core prediction market pain points—and is also a tradable asset on open markets. Unlike prediction platform tokens like Kalshi or Polymarket, which lack native tokens, or objective-price oracles with tokens serving only price feeds, AIA uniquely combines usability and investability in the AI subjective oracle segment. This positions AIA as a rare, perhaps the only, candidate to directly index the prediction market’s growth.

How to participate in the prediction track?

The current prediction scene is clearly shifting toward application narratives at the front end, with value gradually settling into the infrastructure layer at the back. Platforms like Polymarket and Kalshi prove the existence of the track through real trading volume, but what can be priced long-term is likely the underlying layer—namely, the judgment and settlement mechanisms like AI oracles, agent networks, and their associated functional tokens.

As prediction applications attempt to handle more complex, subjective judgments, their demand for AI oracles will increase and become more frequent. This demand will translate into ongoing use of infrastructure like DeAgent AI, and the associated utility tokens—used for payments, settlements, and staking—will accrue value accordingly. The key question then shifts from “whether to participate” to “how and at what level to participate.”

A relatively clear approach is: application layer participants continue to use platforms like Polymarket as alpha tools, betting on specific events; infrastructure layer participants hold positions in tokens like AIA to align with the long-term proposition that AI prediction oracles will become standard in prediction markets. The former addresses whether the trade can make money; the latter ensures that as the track grows, the underlying infrastructure and tokens will also appreciate.

Of course, AIA is just one factor in the broader picture, not a substitute for risk management. A more prudent approach is to treat it as part of the prediction track’s infrastructure index within your risk budget—giving this long-term narrative a place and time to be validated by market judgment.