The Future of the Oracle Machine Track: The Second Rise Curve from DeFi to RWA

Oracles are a key infrastructure that connects on-chain smart contracts with off-chain data in blockchain systems. Essentially, the core competitiveness of oracle projects is reflected in four aspects: the comprehensiveness and reliability of data acquisition, the acquisition of exclusive data and value capture, the security and decentralization of the verification mechanism, and the transmission efficiency and network robustness.

The importance of Oracle Machine to the DeFi ecosystem is self-evident; various lending, staking, and derivative exchanges require Oracle Machine to provide real-time pricing data. However, the application potential of Oracle Machine goes far beyond financial transaction data. Theoretically, various off-chain data, including real estate prices, sensor data, and biomolecular data, can be uploaded to the chain via Oracle Machine to trigger transactions of corresponding assets. This indicates that there are multiple potential rapid rise directions in the Oracle Machine track.

1. The Development History of Oracle Machine

Before 2014, the demand for Oracle Machine began to emerge, and projects like Augur started to explore bringing real-world events on-chain.

In 2015-2016, Ethereum co-founder Vitalik Buterin proposed the concept of Oracle Machine, Oraclize( now launched the first decentralized Oracle Machine service as Provable).

In 2017-2018, the Chainlink project was established, proposing the concept of a decentralized Oracle Machine network (DON), aimed at solving the single point of failure problem.

From 2019 to 2021, the DeFi Summer arrived, and Oracle Machines became the core external data providers. Oracle projects such as Band Protocol and Tellor were launched one after another, aside from Chainlink.

In 2022-2023, Oracle Machine services expanded to multi-chain and multi-scenario. Chainlink launched the cross-chain interoperability protocol (CCIP), and Pyth Network went live on the mainnet.

In 2024-2025, Oracle Machine services will expand from Decentralized Finance to fields such as gaming, NFTs, and insurance. The demand for IoT Oracle Machines will increase with DePIN applications, and AI will begin to explore integration with Oracle Machines.

2. Oracle Machine Track Market Size

By the end of 2024, the total market value of Oracle Machine projects is $10.55 billion, accounting for 11.8% of the total market value of Decentralized Finance. Based on Grand View Research's forecast for the DeFi market, this article makes the following estimates for the market space of Oracle Machines:

• Optimistic scenario: The Oracle Machine market size maintains 10% of the Decentralized Finance market size, reaching $23.1 billion by 2030.

• Neutral scenario: The Oracle Machine market size will remain at 6-8% of the Decentralized Finance market size, reaching 13.8-18.4 billion USD by 2030.

3. Main Projects in the Oracle Machine Track

The current landscape of the Oracle Machine industry can be described as a monopoly. In terms of token market capitalization and total value secured (TVS) (, Chainlink is the absolute leader in this sector. As of March 2025, the TVS of the Chainlink project is $31 billion, accounting for 53.3% of the total TVS in the Oracle Machine sector, while the token market capitalization is $9.6 billion, representing 76.9% of the total market capitalization in the Oracle Machine sector.

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) 3.1 Chainlink

Chainlink is the absolute leader in the Oracle Machine track, providing data feeds ###, automation (, verifiable random function ), cross-chain operation (, and other services. In 2024, data feed service revenue will account for 95% of the project's cumulative revenue, while the other three types of service revenue will account for a total of 5%.

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)# 3.1.1 Chainlink Price Feed Service

Chainlink obtains data from high-quality data aggregators through multiple independent nodes, which is then submitted to the chain after local aggregation and cleansing. Chainlink's aggregation contract further filters and averages the data reported by all nodes to derive the final on-chain price for smart contract calls.

(# 3.1.2 Chainlink Automation

In 2021, Chainlink launched the Keeper trading automation service, and in 2024, it introduced Automation 2.0. Automation 2.0 incorporates cryptographic consensus in decentralized off-chain computing, transferring the expensive computation verification from the blockchain network to the off-chain network, thereby improving the runtime of dApps, reducing costs, and simplifying the user experience.

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(# 3.1.3 Chainlink Cross-Chain Protocol

In 2021, Chainlink released the cross-chain interoperability protocol )CCIP###, providing smart contract developers with a general infrastructure that has computational capabilities, allowing data and smart contract instructions to be transmitted across various blockchain networks.

3.1.4 Chainlink Verifiable Random Function

Chainlink VRF uses Goldberg's verifiable random function to generate one or more random numbers for each random number request, along with a cryptographic proof. This proof will be published on-chain and verified on-chain, and the random number will only be used after the verification is successful.

(# 3.1.5 Chainlink Enterprise Privacy Data Service

Chainlink provides enterprises with the opportunity to sell data and API services in a blockchain environment, enabling various functionalities such as on-chain privacy data, off-chain computation of contract logic, and on-chain trading of privacy data. In November 2024, Chainlink announced the completion of a pilot program with the Global Banking Financial Telecommunication Association )SWIFT### and Swiss banking giant UBS Group (UBS), which tested tokenized fund settlement among the parties.

3.2 Pyth Network

Pyth Network is a project strongly supported by the Solana Foundation, and its fast price update frequency is one of its important labels. Pyth Network supports a total of 65 public chains, second only to Redstone, making it one of the Oracle Machines with the most public chain support.

(# 3.2.1 Pyth Network price feeding service

The competitiveness of the Pyth price feed service is mainly reflected in its low latency and exclusivity. Pyth has not established the role of node service providers in its price feeding, with several financial institutions, including Wintermute and Flowdesk, serving as primary data suppliers for Pyth.

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)# 3.2.2 Pyth Network Random Number Generation Service

Pyth Entropy is a random number generation product launched by Pyth Network. The principle of generating random numbers is similar to Chainlink's random number generation service.

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(# 3.2.3 Pyth Network Fast Relay Service)Express Relay###

The Fast Relay Service is the latest product launched by the Pyth team in July 2024, aimed at eliminating on-chain MEV( maximum extractable value). Through the fast relay approach, the Solana network has added a relay for users to search and bid for prioritized transaction processing, where the highest bidder can allow the node to prioritize this transaction.

3.3 Redstone

Redstone is currently the only data provider that adopts both Push and Pull modes, having completed integrations with over 70 public chains, making it the project with the most integrated public chains among Oracle Machine projects.

(# 3.3.1 Redstone Push Model

The push model consists of two main parts: off-chain relayers and on-chain contracts. The off-chain relayer is a service that runs in a customizable way based on environmental variables, regularly checking a defined set of conditions and pushing prices when the conditions are met.

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)# 3.3.2 Redstone Pull Model

On-chain smart contracts actively call the Oracle Machine interface when they need data, requesting the latest off-chain data from RedStone, rather than continuously receiving data push in advance.

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(# 3.3.3 Redstone AI Oracle Machine architecture CLARA

CLARA is a blockchain communication layer framework launched by Redstone, designed to achieve seamless communication between agents. The CLARA agent protocol consists of three key components: the market module, the CLARA SDK, and framework plugins.

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4. RWA Opens the Second Rise Curve of the Oracle Machine Track

As the "data bridge" of the DeFi ecosystem, the Oracle Machine is deeply tied to the rapid rise of the industry. However, as the penetration rate of the DeFi market approaches saturation, the limitations of over-reliance on a single scenario are gradually becoming apparent. Unlike in DeFi, where the main focus is on providing price feeds, the demand for Oracle Machines in RWA is more complex, including asset valuation as well as real-time tracking and on-chain synchronization of physical asset status, making it a new battleground for the extension of Oracle Machine capabilities.

( 4.1 RWA feeding price service demand rise, Oracle Machine becomes the beneficiary

The integration of traditional finance and cryptocurrency technology is one of the biggest narratives of 2024. RWA is currently in the early stages of development, with vast growth potential in the future. Consulting firm McKinsey predicts that by 2030, tokenized real-world assets will reach $20 trillion. As of March 2025, data from the RWA.xyz platform shows that the total asset scale of RWA is $247.4 billion, including stablecoins ), which still has nearly 10 times the growth potential.

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) 4.2 Oracle Machine that has provided services for RWA projects

• Chainlink has supported the price feed for tokenized RWA in Backed Finance.
• Redstone introduces Eurozone government bond ETF data sources to the crypto world.
• Pyth Network has provided price feed services for the RWA platform Ondo Finance.
• The Supra public chain's distributed Oracle Machine protocol ###DORA(2.0 provides RWA pricing services.
• Chronicle Labs launched the RWA Oracle Machine "The Verified Asset Oracle"

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) 4.3 Non-financial assets, high-threshold off-chain data modeling

Non-financial assets refer to assets that cannot reflect prices in real-time and need to reflect their price at a certain point in time through methods such as mathematical modeling. For example, real estate.