Depth Analysis of Oracle Machine Track and Investment Opportunity Outlook

With Vitalik recently discussing the ultimate Oracle Machine issue, oracles have once again become the focus of attention in the crypto community. Oracles currently play an irreplaceable role in various fields such as insurance, finance, random prediction, and the Internet of Things. As long as off-chain data remains valuable, oracles will continue to play a key role as middleware in the Web3 ecosystem.

The main points are as follows:

• With the increase of dapps and platforms, the demand for Oracle Machines to call data sources and APIs will continue to grow. In the future, many dapps may integrate multiple Oracle Machines to obtain off-chain metadata, avoiding single points of failure.

• Although it is difficult to shake Chainlink’s dominant position in the traditional DeFi Oracle Machine field in the short term, there is still competitive space in the remaining market. In addition, some innovative protocols worth noting have also emerged in segmented fields such as L2, credit, NFT, and DID.

• From the perspective of incremental market demand and emerging scenarios, the Oracle Machine track is unlikely to be monopolized by a single player in the long run. There is still vast development space in areas such as off-chain computing, non-standard asset pricing, and the integration of AI/ML.

Oracle Machine Concepts and Classification

The Oracle Machine is middleware that provides trusted off-chain data services for blockchain projects. The core problem it solves is how to help blockchains trust information from the real world, rather than simply acquiring information. The Oracle Machine injects trust into off-chain data through forwarding, verification, and filtering.

Oracle Machines can be classified into software oracles and hardware oracles based on form, and centralized oracles and decentralized oracles based on data source. Decentralized oracles are usually more trustworthy but less efficient.

Application Scenarios and Track Map

The Oracle Machine is applied in various Web3 scenarios such as DeFi, NFT, GameFi, Social, DAO, and cross-chain. It can be classified from three dimensions: functionality, data sources, and data processing methods.

1. Function: DeFi Oracle Machine, Credit Oracle Machine, NFT Oracle Machine, Identity Oracle Machine, etc.
2. Data Source: First-party, Third-party, Multi-party Oracle Machine
3. Data Processing: Based on game theory, reputation, staking, cryptography, and aggregated Oracle Machine

Investment Opportunity Analysis

1. In the traditional DeFi Oracle Machine space, it is difficult to break the Chainlink monopoly in the short term, but there is still about 10% market competition space.

   • Innovation in consensus mechanisms, such as those based on game theory combined with PoS/PoW, or in the form of DAO committees.
   • Significantly enhance the cost-performance ratio of the feeding price

2. There are potential opportunities in subfields such as L2, credit, NFT, and DID:

   • L2 Oracle Machine focuses on low latency and high security, suitable for high-frequency derivatives trading
   • The credit Oracle Machine combined with on-chain credit expansion theory may trigger an explosion in the next cycle.
   • NFT Oracle Machine can explore off-chain valuation solutions that combine AI/ML.
   • DID Oracle Machine has long-term high value, but currently most are centralized solutions.

3. Case Analysis:
   • Redstone: Low-cost DeFi Oracle Machine, permanently store data on Arweave
   • HyperOracle: ZK-based L2 Oracle Machine, providing unique zkAutomation services
   • CanDID: An Oracle Machine based on MPC and ZK for DID, providing privacy-preserving authentication.
   • CreDA: Multi-Chain Credit Rating Oracle Machine, integrating on-chain and off-chain data
   • Upshot: AI-based NFT valuation Oracle Machine, providing real-time price tracking

Overall, with the development of blockchain technology, oracle machines will continue to play a key role in the next cycle. There is enormous innovative potential in areas such as off-chain computation, pricing of non-standard assets, and the integration of AI/ML, which are worth ongoing attention.