1.Why Will Prediction Markets Become the New Growth Carrier in 2024–2025?

The collective warming of prediction markets is not an isolated event. It is happening at a clear cyclical turning point: DeFi growth has entered a zero-sum phase, the emotion-driven momentum of Memes is beginning to fade, and public chains and capital are once again searching for application forms that are high-frequency, sustainable, and independent of pure sentiment.

The emergence of prediction markets in this cycle is essentially not a “new gameplay,” but a switch in narrative and demand structure.

1.1 DeFi: Scale Remains, but the Imagination for “New Growth” is Shrinking

Entering 2024–2025, DeFi has not declined, but its growth mode has changed significantly.

Capital and users are highly concentrated in top-tier protocols. New active addresses and fee contributions show a strong “head effect,” while the marginal contribution of long-tail protocols to overall growth continues to decline. At the same time, DeFi is severely involutionary—whether it’s restaking, LRT, or yield aggregation—essentially revolving around the redistribution of risk and return for the same set of core assets, rather than introducing new participation motivations.

From a chain-level perspective, this trend is highly consistent. This does not mean DeFi has failed, but that it has completed the initial stage of building “financial infrastructure” and has entered a mature period dominated by efficiency optimization and competition for existing stock.

Such a system naturally has limited appeal for new attention and new users.

1.2 Meme: Sentiment Remains, but It No Longer Has the Capacity to Carry the Cycle

Memes were once the most explosive track in 2024, but entering 2025, their structural problems began to manifest centrally.

First, the price discovery cycle has been extremely compressed. Consensus formation for most Memes is completed in a very short time, and subsequent trading quickly degenerates into a zero-sum game. Participant behavior has shifted from “forming consensus” to “front-running the exit.”

Second, the marginal effect of chain-level incentives has declined significantly. Memes can boost active addresses and transaction numbers in the short term, but it is difficult to settle a developer ecosystem, achieve long-term retention, or build platform mindshare. After experiencing multiple rounds of Meme cycles, public chains have clearly become cautious about investing resources in this model.

Memes have not disappeared, but their role has degenerated from a “growth engine” to a cyclical emotional tool, making it difficult to continue carrying ecosystem-level growth expectations.

1.3 Prediction Markets: From “Valid Demand” to “Chosen by Platforms”

While the marginal effects of DeFi and Memes are declining simultaneously, prediction markets have completed self-consistency at the demand level.

They do not rely on a single market cycle, nor do they need continuous emotional stimulation. Instead, trading motivations naturally arise around events constantly happening in the real world and the on-chain world. This makes it one of the few tracks capable of maintaining activity even in low-volatility environments.

At the same time, prediction markets are not lightweight applications:

• High-frequency settlement requires stable on-chain throughput and low latency.
• Pricing multiple events in parallel relies on continuous liquidity and composability.
• The settlement of real-world results is highly sensitive to security and credibility.

Once the scale expands, it will inevitably form a deep coupling with the performance, fee structure, and infrastructure of the public chain. This also means that prediction markets cannot remain in a state of “chain indifference” for long—they will naturally be included in the strategic vision of the platform.

2.Why is Every Chain Supporting Its Own Prediction Market Leader?

If Chapter 1 answered “why prediction markets are valid in this cycle,” then the more critical question is: Why are almost all mainstream public chains choosing to push prediction markets to the forefront at the same time?

Solana, Ethereum L2s, Base, Arbitrum, Optimism, and emerging modular chains are almost all explicitly supporting an “ecosystem-level prediction market,” giving them structural advantages through funding, traffic, and product integration. This is not betting on a single application, but a collective judgment made by public chains in the current cycle regarding “what kind of application deserves to be an infrastructure-level component.”

2.1 Prediction Markets Are Not Application Innovation, but the Next Class of “Basic Trading Needs”

From the perspective of a public chain, prediction markets possess a rare combination of attributes: High-frequency, continuous, multi-event, non-unidirectional consensus.

DeFi interaction relies heavily on market volatility; Memes rely on emotional outbursts; whereas the trading motivation of prediction markets comes from judgment itself.

Events will not stop:

• Macro policies, elections, regulatory changes;
• On-chain upgrades, token issuances, governance votes;
• Protocol parameter adjustments and systemic risks.

These events possess natural continuity and uncertainty, forcing users to repeatedly express their stance and constantly correct their judgments. For public chains, this means prediction markets are not creating one-time activity, but building a long-term operating judgment engine on-chain.

In a cooling market, it provides basic transaction density; in an active market, it amplifies trading intensity. After DeFi and Memes, prediction markets have become one of the few application forms that can simultaneously carry information, emotion, and capital.

2.2 Public Chains Supporting Prediction Market Leaders is Essentially Competing for “On-Chain Expectation Pricing Power”

Prediction markets are not applications where multiple points can coexist; they are an infrastructure where only one can win.

Once liquidity is dispersed, prices become distorted; if prices are distorted, users will quickly migrate. Therefore, prediction markets naturally follow a single-point convergence logic rather than multi-protocol coexistence. This means that for public chains: supporting multiple prediction markets is equivalent to actively giving up pricing power; explicitly betting on one leader is equivalent to building an expectation pricing hub within the chain.

More importantly, prediction markets compete not on trading volume, but on cognitive density. They transform viewpoints into positions, compress emotions into prices, and condense scattered judgments into signals that everyone must face.

When a chain has a stably operating prediction market, what it gains is not just a financial product, but a set of on-chain expectation systems: looking at odds before a project launch, risk changes reflected first in prices, and signals appearing before governance decisions. This is also the reason why prediction markets leap from “ecosystem applications” to cognitive layer infrastructure. The competition between public chains is also shifting from “who has more protocols” to “who can define future expectations.”

3.Who is Likely to Survive? Three “De-Bubbling” Screening Indicators

Prediction markets that can truly cross the cycle must complete three leaps.

We can make a quick judgment on Polymarket, Limitless, Opinion Labs, Kalshi, and Probable based on three dimensions: product stability, compliance recognition, and ecosystem formation. Kalshi and Polymarket have the highest certainty of survival, Opinion Labs has differentiation opportunities, while Limitless and Probable carry significantly higher risks.

3.1 Product Performance Stability (Foundation for Survival)

The core lies in the maturity of the technical architecture, the reliability of trading and settlement, and the system’s ability to resist risk, which directly determine user trust.

Project	Compliance & Accessibility	Risk Factors	Score (Out of 5)
Polymarket	Polygon / Solana dual-chain deployment, multi-signature + UMA / Chainlink oracles, stable order placement under high traffic, contracts audited multiple times, fault recovery < 2h	Cross-chain synchronization latency; liquidity may temporarily shrink under extreme market conditions	4.5
Limitless	Base chain CLOB model, Pyth oracle, stable under low traffic in early stage, but matching efficiency decreases under high concurrency; historical fault recovery > 4h	Insufficient technical team experience, reliance on third-party infrastructure, weak fault-tolerance mechanisms	2.5
Opinion Labs	Monad chain vAMM + order-book hybrid architecture, decentralized oracle network, low trading latency; however mainnet is not yet stable, long-term stress test data insufficient	New chain ecosystem compatibility still needs validation; complex contracts prone to bugs	3
Kalshi	Centralized architecture + Solana on-chain backup, CFTC-compliant disaster recovery, records of trading / settlement failures, system response < 100ms	Single-point dependency in centralized architecture, requires continuous investment in operations and defense against attacks	4.8
Probable	BNB Chain zero-fee pre-settlement, PancakeSwap technical support; however TVL is low, node stability is average, settlement congestion has occurred in the past	Strong dependence on incentives, risk of capital retention, slow technical iteration	2

Conclusion: Kalshi > Polymarket > Opinion Labs > Limitless > Probable. Centralized compliant platforms have an advantage in stability, while emerging chain projects lack technical maturity.

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3.2 Compliance and Global Recognition (Lifeline for Survival)

Compliance is not a bonus item; it is the determining factor of business boundaries.

Project	Compliance & Accessibility	Risk Factors	Score (Out of 5)
Polymarket	Approved by the CFTC, KYC/AML implemented across multiple countries, frequently cited by mainstream media, institutional capital gradually entering	In some regions (e.g. the EU), compliance frameworks are still evolving; sensitive audits are stringent	4
Limitless	Within Base chain compliance framework, basic KYC, no top-tier regulatory licenses, moderate media exposure	Has not proactively engaged with regulators; vulnerable to policy tightening or sudden regulatory changes	2
Opinion Labs	Currently applying for regulatory approval across multiple jurisdictions, focused on AI model consensus layer, compliance pathway still unclear	Innovative business model; regulatory definitions remain ambiguous, close to compliance gray areas	2.5
Kalshi	CFTC-registered DCM, approved for U.S. market entry, formed prediction market alliances with Coinbase / Robinhood, high-frequency use by institutional researchers	Compliance currently limited to the U.S.; global expansion requires country-by-country applications with high costs	4.5
Probable	Within BNB Chain compliance framework, no independent regulatory licenses, relies on chain-level compliance, low media visibility	Systemic chain-level compliance risks; restricted or banned access in some regions (e.g. the U.S.)	2

Conclusion: Kalshi > Polymarket > Opinion Labs > Limitless ≈ Probable. Compliance qualification is the core barrier for long-term survival; projects without regulatory endorsement carry extremely high risks.

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3.3 Ecosystem Formation (Amplifier for Survival)

True barriers come from real user structures, stable liquidity networks, and long-term cooperative relationships.

Project	Compliance & Accessibility	Risk Factors	Score (Out of 5)
Polymarket	Retail + institutional users; cumulative trading volume exceeds USD 36 billion; integrated with DeFi protocols; community interaction rate > 8%	Low institutional participation ratio; ecosystem incentives rely heavily on token airdrops	4
Limitless	Early-stage volume inflation driven by incentives; user retention < 10%; no core institutional partnerships; community interaction rate only 3%	Hollow ecosystem prosperity; few real users; lack of stickiness	1.5
Opinion Labs	Focuses on AI model consensus layer; collaborates with research institutions; community interaction rate 6%, but total user base is small	Unclear commercialization path; willingness to pay on the B2B side remains unproven	3
Kalshi	Institutional users account for > 40%; CFTC backing attracts hedge funds and quantitative teams; co-founded industry alliance with Crypto.com and others	Slow growth of retail users; weak community engagement	4.2
Probable	Traffic inflow from BNB Chain; acquires users via zero-fee settlement; however, core feature usage rate < 30%; community interaction rate 4%; no institutional participation	Strong reliance on ecosystem support; weak organic growth; users are mostly short-term “airdrop hunters”	1.8

Conclusion: Kalshi > Polymarket > Opinion Labs > Limitless > Probable. Institutional and real user ecosystems are key to sustainable growth; ecosystems driven by incentives are prone to collapse.

Summary:

• Inevitable Survival: Kalshi (perfect loop of compliance + stability + institutional ecosystem), Polymarket (dual-chain + compliance + retail ecosystem, strong fault tolerance).
• Chance of Survival: Opinion Labs (AI consensus layer differentiation; if compliance lands + user growth occurs, it can occupy a niche market).
• High Risk of Elimination: Limitless (weak in both technology and compliance, fake ecosystem), Probable (relies on incentives and traffic, no core barriers).

Recommendation: Prioritize positioning in Kalshi/Polymarket; pay attention to Opinion Labs’ compliance progress; avoid purely incentive-driven projects like Limitless/Probable.

Prediction markets are a track that has just been activated on-chain. Who ultimately survives depends on the final background and the operators, which are the keys to ultimate victory. Historically, small ants have chewed down elephants. The suggestions above are only partial conclusions based on the current situation. The prediction market track is huge, and more projects will emerge in the future.

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4.Conclusion: Prediction Markets Are Becoming the Next Layer of Basic Infrastructure

The heating up of prediction markets in 2024–2025 is not a short-term narrative rotation, but a structural choice by the crypto ecosystem for “sustainable trading demand” occurring after the maturation of DeFi and the receding of Meme sentiment.

Unlike tracks that rely on market trends and emotions, prediction markets are event-driven and possess the characteristics of high frequency, continuity, and non-unidirectional consensus. This allows them to maintain activity during low-volatility phases and amplify trading intensity during high-volatility phases. At the same time, their requirements for throughput, latency, liquidity, and settlement credibility naturally bind them deeply with the underlying capabilities of public chains, elevating them to strategic-level applications by platforms.

This is also why prediction markets are leaping from “ecosystem applications” to cognitive layer infrastructure: they compress scattered judgments into price signals, becoming an important entrance for on-chain expectation formation and risk foresight. For public chains, supporting a prediction market leader is essentially competing for the pricing power of on-chain expectations, rather than chasing short-term trading volume.

Under this structure, competition in prediction markets will not coexist in a multipolar manner for long. The projects that ultimately survive will inevitably possess a stable product and settlement system, sustainable compliance boundaries, and an ecological network composed of real users and liquidity. Incentives and wash trading can create heat, but they cannot replace trust and structural advantages.

The differentiation of prediction markets will ultimately depend not on who grows faster, but on who can become the long-term operating hub for on-chain expectations. What we see now is only the starting point, not the endgame.