prediction market Oracle Machine: current status and opportunities

TLDR;

On-chain prediction markets are booming, and oracles represent the best infra-related opportunity. As the core settlement mechanism, oracles determine what themes prediction markets can support and whether they can operate correctly and efficiently.

Currently, Polymarket's oracle is dominated by UMA, supporting subjective prediction markets that account for 80% of the market size, while Chainlink has been introduced to settle the remaining 20% of the price market. Pyth has been introduced to solve the on-chain data issues of Kalshi's prediction market, while other oracle solutions primarily focus on AI.

As the only subjective settlement solution, UMA has established a good barrier in terms of product and operational experience. However, there are still issues such as long settlement times and manipulation by large holders, which essentially limits the ability of prediction markets like Polymarket to explore new types of markets. This provides space for new solutions, including the introduction of AI agents, addressing manipulation issues, continuous/combinatorial market oracles, permissionless/long-tail market oracles, and event-driven DeFi integration for prediction markets.

Background

Crypto has entered the application era, where the infrastructure projects of the previous era were driven by empowering applications. Memecoins have driven Dex infrastructure, AI agents have driven Tee infrastructure, Yield has driven DeFi infrastructure, and the rapidly growing prediction market may drive oracle infrastructure.

Prediction markets are becoming the new growth engine for Crypto. From 2024 to 2025, it will undergo a qualitative change from a niche experiment to a mainstream PMF application.

• During the 2024 U.S. presidential election, Polymarket's trading volume surged from $73 million to $2.63 billion (a 48-fold increase), while Kalshi reached $1.97 billion (a 10-fold increase).
• The overall trading volume of the prediction market has reached $15.7 billion. ICE, the parent company of the New York Stock Exchange, has invested $2 billion in Polymarket, and many well-known hedge funds have entered the prediction market trading or are exploring ways to participate.
• Regulatory-wise, the CFTC has approved Kalshi's election contracts, while Polymarket is re-entering the US market in compliance through the acquisition of QCEX in the form of a mobile app. Polymarket has hinted several times at the potential of its tokens and the crypto ecosystem.

Various factors will act as catalysts, and it is undoubtedly predictable that the market will erupt.

Prediction markets can be divided into two categories: the first involves subjective issues, focusing on news events related to politics, culture, economics, sports, etc., such as presidential elections or World Cup championships. By defining the questions and outcomes using natural language, some conclusions can have a degree of subjectivity, such as “Is Zelensky wearing a suit?” - what type of clothing qualifies as a suit.

Another category is the price market, similar to binary options products for cryptocurrencies/stocks, but with a simpler and more understandable model. For example, “Can the price of BTC reach $XXX at a certain moment?”

Currently, UMA and its optimistic oracle are the only providers of subjective market settlement. There are currently no other solutions for any decentralized, unstructured data or markets that require subjective adjudication. UMA addresses this issue through an optimistic solution similar to optimistic rollups (proposing a result that defaults to approval if there is no dispute, and further adjudicating penalties if there is a dispute).

Structured data oracle services, with price data markets at the forefront. Markets such as “BTC's price reaches $XXX at a certain moment” can be better resolved directly through price oracles like Chainlink. In fact, Chainlink's routing for price disputes has already existed in UMA's previous escalation manager for resolving disputes. The direct collaboration between Polymarket and Chainlink allows for quicker resolution of this.

Currently, there are still many mechanisms and user experience improvements for prediction market oracles. These include settlement time, incentive models, continuous data, permissionless settlements, and so on. Prediction markets will bring new oracle products and opportunities for architectural innovation.

Why Oracles Are Important

Settlement can be categorized into centralized and decentralized types. The vast majority of early prediction markets adopted centralized solutions. Decentralized solutions come with high costs and are difficult to execute. However, in order to keep prediction markets free from single point control and to ensure that the value of the “truth media” of prediction markets is recognized, decentralized settlement solutions are required, which again rely on oracle.

This bottleneck technology determines whether an on-chain prediction market can continue to operate independently and support a large-scale market. This is also why BSC needs to solve the oracle problem before launching prediction market projects.

At the same time, to enable the data from the prediction market to generate value and circulate on the chain, the help of oracles is also needed. Oracles can use the results from the prediction market as data sources for on-chain use. The collaboration between Kalshi and Pyth mainly revolves around this point.

Using market data as a primitive, on-chain application developers can create entirely new products based on this. Examples given by Pyth officials include:

• Develop futures markets based on real-world events. These protocols can use Kalshi's real-time odds as a baseline reference, automatically adjusting prices as the source market changes.
• DeFi protocols can build conditional products that respond to real-world probabilities.
• Insurance products linked to political outcomes;
• NFT series evolved based on election results;
• Games tournament to unlock the prize pool when specific events occur.

Current Prediction Market Oracles

Understanding UMA's current monopoly position can be measured by predicting the TVS in the market. TVS (total value secured) measures the total value secured by the oracle, with Polymarket currently holding an 80% market share and Chainlink accounting for the remaining 20% of the pricing market.

▲ source: Defilamma

In terms of business model, Stable adopts a strategy of prioritizing recent market share expansion over revenue by using USDT payments with no Gas fees to attract users and establish payment traffic. In the long run, profitability will mainly come from within its consumer applications, supplemented by select on-chain mechanisms.

In addition to USDT, Stable has also seen significant opportunities from other stablecoins. With PayPal Ventures investing in Stable at the end of September 2025, as part of the deal, Stable will natively support PayPal's stablecoin PYUSD and promote its distribution, allowing PayPal users to “directly use PYUSD” for payments, and Gas fees will also be paid in PYUSD. This means PYUSD will also be gas fee-free on the Stable chain—this will extend the ease of operation of the USDT payment track that attracts PSPs to PYUSD as well.

UMA

Polymarket currently uses UMA's MOOV2 ( Managed Optimistic Oracle V2 ) to settle markets. When the market is due for settlement, it will first be closed, and the proposer will submit the results. If the result is not challenged during the dispute window, it is considered the correct result. If it is challenged, UMA's decentralized arbitration mechanism will intervene to make a ruling.

UMA's optimistic oracle has gone through four versions, evolving from its initial focus on synthetic assets to continuously adapting to prediction markets:

Currently, Polymarket supports the MOOV2 contract. This change was made after UMA's governance proposal UMIP-189 was passed on August 6. Previously, one issue with OOV2 was that many proposals were submitted too early and in a lack of experience, which often led to disputes, causing market settlements to be delayed by several days.

The entity behind UMA, Risk Labs, has announced an initial whitelist containing 37 addresses. The whitelist includes employees of Risk Labs and Polymarket, as well as users who have more than 20 proposals with an accuracy rate exceeding 95%. This is the current prototype of UMA's “elite” governance.

As the most widely used oracle, UMA's multiple iterations reflect its deep understanding of the prediction market use case and its robust ecosystem and infrastructure. However, UMA's performance is not perfect at the moment, and the issues mainly stem from two aspects:

1. Large Trader Manipulation Risk
2. The time to finalize the results is long.

In terms of managing risk, UMA's DVM (Data Verification Mechanism) relies on token holder voting to determine data results. Although there is a minimum voting amount (GAT, approximately 5 million UMA) and a voting consistency threshold (SPAT, 65% consistency) to ensure security.

However, due to the low market value of the tokens and the highly concentrated distribution, large holders can easily influence the voting results. In 2024, a market on the Polymarket platform regarding “Will Ukraine sign a mineral agreement with the United States?” was ruled “YES” by UMA, even though the reality was not so.

On-chain data shows that a single whale cast about 5 million UMA through multiple addresses, accounting for approximately 25% of the total voting volume; only two whales hold more than half of the valid voting rights. This concentration structure leads small voters to tend to “follow the whales” to avoid penalties. The penalty ratio for erroneous voting in UMA is only about 0.1%, which is very low cost, significantly increasing the actual risk of manipulation by large holders. Currently, UMA's market cap is 100m, but the OI of the polymarket it supports is >200m, reflecting the asymmetry of economic relationships and providing space for malicious manipulators.

Secondly, in terms of result confirmation speed, UMA's dispute resolution process is relatively lengthy. Any data request must go through an “active period” after submission, and can only be automatically confirmed if there are no challenges; if challenged, it enters the DVM voting phase, which typically lasts 48 to 96 hours. If the threshold is not met, a new round of voting must be initiated, which can lead to settlements being delayed by several days.

In scenarios that require quick settlements, such as prediction markets and leveraged products, this delay issue is particularly evident. Users' funds are locked and cannot be reused, while also increasing the arbitrage opportunities presented by information lag.

UMA has advantages in decentralization and censorship resistance, but issues such as high token concentration and long settlement periods pose risks of manipulation and efficiency bottlenecks. To play a mainstream oracle role in broader prediction scenarios, UMA needs further optimization.

UMA is currently exploring new disruptive architectures, collaborating with EigenLayer to research and develop the next generation of oracles utilizing Eigenlayer's staking system. At the same time, it is also making some new attempts in AI. The Optimistic Truth Bot is a proposer agent in the prediction market. It listens to questions on Polymarket and proposes the most likely answers in real-time, 24/7, while waiting for challenges, significantly reducing settlement time. You can find specific markets through the @OOTruthBot Twitter account.

Chainlink

Chainlink is a well-established DeFi oracle service provider, whose product capabilities actively acquire and aggregate off-chain data (such as prices) from multiple sources and transmit it on-chain through a network of nodes. Currently, Polymarket is collaborating with Chainlink to serve the price data prediction market.

In the previous UMA's Escalation Manager dispute escalation system, Chainlink's routing was involved. This means that Polymarket has actually been a user of Chainlink for a long time, and the current integration with Chainlink makes this even more direct.

Pyth

Pyth is currently collaborating with Kalshi, primarily to transmit Kalshi's data. Kalshi's data is regulated by the CFTC, thus its value lies in transmitting compliant data, mainly including sports and economic data as data sources. This is similar to compliant casinos selling their real-time sports event data to downstream.

New Player

The vast majority focuses on providing verification services through AI. Currently, the role of the Agent is more inclined towards submitting resolution intentions, but since AI can be online 24/7, it ensures that any market, especially some high-frequency markets created without permission, can achieve effective settlement. The earlier text mentioned that UAM is exploring solutions proposed by AI participation through OO AGENT.

A similar example from Solana is the XO market. It uses AI models to extract analytical data from trusted APIs (real-time news, sports data sources, etc.) and quickly resolves Yes/No questions through pattern recognition, achieving a relatively high success rate. Some oracle projects on BSC that CZ recently mentioned are also exploring this direction.

What Opportunities Are There for Oracles

The outbreak of prediction markets has raised higher demands on the support range, intelligence, real-time performance, and incentive design of oracle systems. Currently, mainstream oracles still face bottlenecks: UMA's settlement time is 24-48 hours, the average dispute resolution cycle takes several days, and the dispute settlement rate is high, with voting power concentrated among large holders… There is still room for optimization regarding centralization and efficiency, and the types of markets they can support are still limited by their architecture, which may be the biggest obstacle affecting Polymarket's innovation in market types.

AI Assisted

AI can understand natural language, which is very suitable for markets such as politics, sports, or social events. In the past, human judgments often led to significant semantic differences and subjective factors, resulting in frequent disputes. AI Oracle can significantly improve this issue through multi-source verification and neutral language models.

Anti-Manipulation

UMA token holders are both voters and stakeholders, creating structural conflicts. Large holders can influence voting outcomes with as little as 5 million UMA. From a token design perspective, how to ensure sufficient economic security, and beyond token design, how to establish mechanisms against manipulation (such as real-time marking of malicious addresses, detecting group malicious behavior, etc.)

Multi-stage and subjective predictions, real-time data and continuous price sources

The prediction market has long focused on binary settlements, leading to a significant dimensionality reduction of information in the prediction market. Future socially aware oracles will need to access more data sources and adopt dynamic models for different data to conduct comprehensive evaluations. Through discussions with DeFi projects related to Polymarket, I realized that there is a significant design space for dynamic settlement data during ongoing market activities. Supporting more sustainable prediction markets, such as real-time dynamic in-game trading for sports events, presents substantial opportunities in continuous pricing markets or combination markets like parlay, but currently, oracles do not support this.

Permissionless Expansion and Long Tail Market

Future permissionless prediction markets will achieve asset creation rates similar to pumpfun, and the massive market settlement demand will render UMA's current manual review upgrade model inoperable. How to quickly address the creation, settlement, and liquidity dispersion issues of long-tail markets could be resolved from a top-down perspective through oracles.

Event-driven DeFi integration

After the combination of prediction markets and DeFi, the on-chain event probabilities can directly impact the pricing of lending and derivatives. For example, automatically reducing lending leverage when the “interest rate hike probability > 90%”. Perhaps innovation in the DeFi space can be brought about by oracles + prediction markets.