Author: Chloe, ChainCatcher

In the past two years, prediction markets have become the most prominent narrative in the cryptocurrency industry. The total trading volume in the entire sector approached $10 billion by the end of last year, with monthly growth momentum significantly accelerating in the second half of 2025.

However, on the other side of this celebration, there exists a role that has consistently remained in the shadows, repeatedly criticized by users: the oracle.

UMA's Double-Edged Sword

Over the past year, several significant controversies surrounding Polymarket emerged, such as whether Ukrainian President Zelensky wore a suit (with a total trading volume of $237 million), the Ukraine mineral agreement (involving $7 million, with a major user holding about 5 million UMA to manipulate voting), and whether the Trump administration would declassify UFO files in 2025 (a $16 million market publicly referred to by users as the "whale proof" scam). The source of these controversies pointed directly to the same issue: UMA's Optimistic Oracle and its token governance structure.

The design logic of UMA's Optimistic Oracle is as follows: anyone can propose a result by putting up a stake; if no one raises a dispute during the challenge period (usually 2 hours), the result is deemed true by default; if there is a dispute, UMA token holders will vote on the decision through the Data Verification Mechanism (DVM).

The advantages of this mechanism are obvious: it's cheap, it can handle long-tail events, and it can address "subjective issues," like whether "Zelensky's outfit counts as a suit," which traditional price oracles cannot handle at all.

However, the disputes around Polymarket revealed flaws in this design. For instance, in the Ukraine mineral agreement event last March, the total trading volume of this prediction event was about $7 million, focusing on whether Trump would reach a rare earth mineral agreement with Ukraine before April.

Despite no agreement being reached, the market was still settled as "Yes." According to reports from The Defiant and Cryptopolitan, the main reason was that a major UMA holder controlled approximately 5 million UMA across three accounts, representing about 25% of the voting weight, and steered the vote toward Yes. Subsequently, Polymarket made it clear in a Discord announcement: "This is not a system failure, but a result of the governance mechanism in operation, and therefore, refunds are denied."

It can be said that Polymarket's reliance on UMA is facing systemic risks. Originally designed as a "neutral truth adjudication layer," the centralized distribution of governance tokens has become a tool for a few individuals to influence market outcomes.

According to cryptocurrency data platform RootData, until September of last year, Polymarket began to promote cryptocurrency events and urgently needed to introduce a more deterministic data source. As a result, it started to delegate some settlement work to a completely different oracle system, Chainlink.

Chainlink: Another Dilemma of the Leader

CoinDesk reported that Polymarket began to introduce Chainlink to improve its determination of prediction outcomes. The two sides announced that Polymarket would use Chainlink to automate the settlement of markets related to asset prices, reducing delays and the risk of tampering. Initially focusing on cryptocurrency asset price markets, they also explore application space in more subjective markets.

The significance of this cooperation lies in Polymarket shifting from relying on UMA's "crowd-gaming style subjective consensus adjudication" to a path where Chainlink can directly read market prices and automate determinations.

In terms of market landscape, Chainlink is indisputably the leader in the oracle sector, with over 87% market share in the oracle market and a TVS share of 61.58% (about $62.9 billion), marking a significant gap between it and the second place, Chronicle (10.15%), and the third place, RedStone (7.94%).

It can also be said that its penetration in DeFi is almost saturated. Mainstream protocols, from Aave, GMX, Synthetix with liquidation and pricing to Curve's safety reference and Lido's cross-chain standard, almost all adopt different services provided by Chainlink.

Market share is reflected in its layout. Chainlink provides 2,000 price feeds across about 27 chains (an on-chain permanent price feed service) and has deployed Data Streams (low-latency, on-demand verification high-frequency feed service) on 37 networks; the CCIP (Chainlink Cross-Chain Interoperability Protocol) mainnet has covered 70 public chains and L2, with about 200 cross-chain tokens registered as CCIP standard available for use.

This scale signifies that Chainlink has expanded itself from a "single-chain price feed intermediary" to a "layer for information and asset exchange between multiple chains."

However, saturation also means that DeFi is no longer its growth curve. According to a deep report from Galaxy, about 97% of Chainlink's cumulative revenue (about $399 million) comes from Price Feeds, while VRF (Verifiable Random Function, used for NFT minting and on-chain gaming), Automation (automated execution), and CCIP only account for about 1.5%, 0.6%, and 0.5% respectively.

In other words, Chainlink's cash flow is highly concentrated in the most mature and commoditized price feed business, which has reached market saturation, leaving extremely limited marginal growth potential.

In response, Chainlink has bet on three incremental growth paths.

The first is RWA and institutional finance.

As observed from Chainlink's cooperation matrix, it has previously collaborated with Swift and several institutions to complete cross-chain experiments for tokenized assets; last year, it worked with 24 major financial institutions to advance plans for on-chain corporate actions, with the DTCC Smart NAV pilot distributing mutual fund NAV data on-chain.

The same year, Chainlink partnered with Mastercard to enable on-chain crypto purchase processes for over 3 billion cardholders; the U.S. Department of Commerce (BEA) has also put core macroeconomic data such as GDP and PCE on-chain through Chainlink Data Feeds, initially covering 10 public chains.

The second path is CCIP cross-chain communication.

CCIP has become one of the choices for cross-chain standards. Kinexys, a subsidiary of JPMorgan, has collaborated with Chainlink and Ondo to complete the cross-chain DvP settlement experiment for tokenized U.S. Treasury bonds; Aave uses it to promote GHO cross-chain, and Lido adopts it as the official cross-chain standard for wstETH; the same year, CCIP also launched on Aptos, extending its reach to the Move ecosystem.

By October 2025, CCIP's cumulative token transfer volume is nearly $2 billion.

The third path is prediction markets and "event settlement finance."

Polymarket's integration marks the beginning of this path. It signifies Chainlink's expansion from serving only "asset prices" to the broader field of "event settlement." As demand for predictive markets in U.S. stocks, commodities, ETFs, macro indicators, and other categories that can be settled automatically surges, Chainlink has identified a natural extension for its original pricing business.

Overall, while Chainlink occupies a leading position in the market, the growth of traditional DeFi price oracles has peaked; it must rely on RWA, institutional finance, CCIP, and the financialization of prediction markets to rebuild its next growth curve.

The potential on these paths is considerable. According to BCG, the scale of RWA tokenization could reach $16 trillion by 2030, and SWIFT processes $150 trillion in settlement volume each year, but the realization cycle is measured in "years," while token holders' patience is typically measured in "days."

This mismatch may represent the core pressure that Chainlink, as a leader, will still face in 2026.

Multiple Oracles Eating Up the Prediction Market's Pie

At the beginning of April this year, Polymarket announced a partnership with Pyth Network.

On this platform, there are prediction markets for commodities such as gold, silver, WTI crude oil, natural gas, as well as U.S. stocks like NVDA, AAPL, TSLA, COIN, PLTR, and predictions for major indices and ETFs, with settlement data provided by Pyth in real-time via WebSocket, which Polymarket samples once per second.

As a first-party data provider (market makers and institutions like Jump Trading, Jane Street, Blue Ocean, LMAX, etc. publish data directly), Pyth adopts a pull model, delivering data with low latency to the application layer.

This structural division is not just a choice unique to Polymarket. Kalshi, which is regulated by the U.S. CFTC, has also integrated Pyth as the settlement data source for its newly launched commodity center, covering commodities such as gold, silver, Brent crude oil, natural gas, copper, corn, soybean, and wheat; Pyth Pro also provides direct market data access to Kalshi's market makers, and it will further expand to indices, stocks, and foreign exchange.

When both Polymarket and Kalshi choose Pyth as the settlement layer for traditional financial assets, it reflects a trend not just as a unique engineering decision for individual platforms, but as a shared demand in the entire prediction market sector for a "high-frequency data settlement layer at the institutional level."

Pyth has thus secured a portion of the market in this area, but this position is a subset of "traditional financial asset events," with Chainlink serving the crypto domain and UMA addressing subjective matters.

From this three-tiered structural division, one can observe the realities of the oracle sector exposed by prediction markets.

First, no single oracle can fully serve a mature prediction market.

UMA's community adjudication mechanism cannot handle high-frequency prices; Chainlink's on-chain feed model is not an optimal solution for millisecond-level event settlements; although Pyth has clear advantages in low-latency pricing, it cannot entirely address textual type issues.

Second, every time Polymarket introduces a new oracle, it is expanding the map of "tradable events."

From UMA's non-standard events to Chainlink's crypto assets, and then to Pyth's traditional financial assets, each step incorporates more uncertainties from the real world into the range of on-chain betting. Following this logic, in the future, macroeconomic indicators (GDP, CPI, interest rate decisions), central bank interest rate decisions, listed company earnings, and even AI model releases may all become market categories for Polymarket.

As long as there exists a verifiable data source, corresponding markets can be constructed.

Conversely, for oracle projects, this also means that the wild expansion of prediction markets will not allow any single oracle to reap all the benefits. Every new market will be allocated to the oracle "most suitable for handling that type of data structure," resulting in multiple parties sharing the pie without overlap.

Conclusion

The oracle sector, by 2026, has essentially evolved from the early "data pipeline" stage to a "verifiable fact layer" that supports the entire on-chain economy.

Its service targets are no longer just for DeFi liquidations and collateral valuations, but also include compliance verification for RWA on-chain, trustworthy transmission of cross-chain information, and settlement of prediction markets for real-world uncertainties.

Prediction markets serve as a magnifying glass to observe the intense competition in this red sea.

Polymarket's three-tiered division of labor, along with Kalshi's concurrent choices in traditional financial assets, reveals a reality: no single oracle can fully serve a mature on-chain application. Each topic on the platform will be assigned to the oracle most suited to handle that type of data structure.

Infrastructure differentiation has become a reality. But when no single project can enjoy exclusive benefits, who can truly become irreplaceable?

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