In a world where misinformation is rampant—and where most oracle exploits occur not at the protocol level but through manipulation of data sources or assets—it’s essential to leverage available technology to counter these challenges.
The oracle problem extends beyond simply ensuring data delivery. The oracle problem can be broken down into 3 layers:
- Integrity – Data quality and methodology
- Infrastructure – The oracle protocol
- Integration – Proper implementation by the user
Tellor has been focused on the infrastructure and integration layers for the last few years. As we saw scaling becoming a reality through rollups, L2s, better bridges, app chains, and L1’s, we’ve been working on preparing Tellor for this new environment. Tellor’s new L1, provides the infrastructure that allows it to scale efficiently as the blockchain universe continues to expand. On the integration front, we continue to educate users, provide integration guidelines, and review every implementation we know about (integrations do not require the team’s involvement) to ensure proper usage. But it’s time to give more attention to the integrity layer of the oracle problem and leverage available technologies.
As ZK proofs become more efficient, it can become a great complement to oracles but oracles focused solely on relaying API information could become obsolete or be forced to morph into keeper networks. AI or ZK proofs contribute efficiencies but neither solve the data quality issue – these can’t tell if they are being fed good or bad data. Improving data quality is critical as it will become one of the primary differentiators among oracles going forward.
Integrity: The Next Frontier
With the new Tellor system audited and live on testnet, we’re expanding our focus to data quality. High-quality data must be:
- Relevant – Data must directly answer the query. While reporters can source data from anywhere, economic incentives push them toward the best sources. For price feeds, this process is straightforward, but for more subjective queries, finding relevant information is time-consuming and inefficient.
- Accurate – Our consensus mechanism aggregates data from multiple participants to determine the most accurate value. For numeric queries, users typically define aggregation methodologies, but research into modeling techniques for volatile assets remains uncharted in crypto and could offer significant improvements.
- Consistent – Aggregation must remain consistent for comparability. In Tellor, each queryID is tied to a specification, and reporters face economic penalties for deviation. This ensures data consistency over time.
- Timely – Data must be reported within the timeframe specified by the user. Like consistency, timeliness is enforced through data specifications and the dispute mechanism.
Exploring Improvements in Data Quality
While Tellor’s infrastructure already addresses consistency and timeliness, the most promising areas for improvement within the ecosystem are in the relevance and accuracy of the data. Data quality efforts will be focused on these two areas in the near future:
- Methodology Research
Traditional finance has long explored robust pricing methods for derivatives and volatile assets. Crypto has yet to fully embrace these concepts like uncertainty modeling, worst-case scenario analysis, stochastic control, and distributional robustness—all of which could strengthen oracle data quality. The entire industry stands to benefit from better reference prices, independent economic statistics and robust pricing methods.
- AI Integration
There is potential for AI and oracles to be complementing technologies. AI could play a crucial role in sourcing data, selecting reliable sources, and refining aggregation methods. AI’s efficiency in summarizing large amounts of information could enable reporters to engage with more complex, subjective and even controversial queries. Yet, its non-determinism and susceptibility to misinformation—where bad actors create misleading content to manipulate AI-driven conclusions—raises critical questions about reliability. Its strengths however, could outweigh the negatives and opens up a whole array of interesting research questions:
- Can AI reliably differentiate between trustworthy and misleading data and data sources?
- Could AI enhance outlier detection and aggregation methodologies?
- Can Tellor act as a data curator for AI models, ensuring the integrity of the data they rely on?
Setting a New Standard for Oracles
Tellor’s architecture is uniquely positioned to leverage cutting edge technology, enabling tailored data creation and methodologies that prioritize accuracy and reliability. By fostering a decentralized approach to data collection and validation, we ensure that our users have access to secure, high-quality, and censorship-resistant information, setting a new standard in the oracle ecosystem.
