API3: What is OEV (Oracle Extractable Value)? Why is it important?

Feb 27, 2024

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Can you give us a quick background of the story of API3 and how the OEV network came to launch ?

API3 is an Oracle project established from the founders’ prior experience chainlink nodes and their pursuit to devise a system that is more efficient, direct, and verifiable, which led to the inception of API3. Oracles have a wide range of applications, yet the most recognized use case is price feeds, a critical requirement for the decentralized finance (DeFi) sector. Currently, no substantial revenue is generated from providing price feeds, as the operation is largely subsidized either by Oracle projects through the sale of their own tokens to support the feeds or by networks investing millions into these services. This is why API3 places significant emphasis on identifying self-sustaining niches, allowing an Oracle project to be financially viable. This approach paved the way for the launch of the OEV network, focusing on creating a model where the provision of Oracle services can be profitable in its own right.

What is OEV (Oracle Extractable Value)? Why is it important?

Oracle Extractable Value (OEV) is a specific type of Miner Extractable Value (MEV) focused on the impact of Oracle updates or the lack thereof in blockchain systems. Similar to MEV, where miners can prioritize transactions for personal gain, OEV involves the strategic prioritization of oracles transactions to extract value, particularly in scenarios where Oracles provide critical market data or trigger significant on-chain events like liquidations.

How does OEV Network work?

The discussion highlights the limitations and challenges of current Oracle models, particularly the “push Oracle” system commonly used in lending protocols. This system updates on-chain prices based on predefined parameters, such as a 1% price movement, which can lead to delayed responses to market changes. Such delays can result in users being liquidated at less favorable prices.

To address these issues, the idea is to transition from Oracles pushing data blindly to a competitive model where updates are made when most valuable. This involves creating a market for Oracle updates, allowing individuals to bid on the opportunity to update prices. This model aims to provide more timely and accurate data, potentially improving the execution of liquidations and other dependent functions.

The OEV network seeks to enhance this further by enabling more efficient liquidation processes. By allowing participants to update prices at critical moments, users could be liquidated at more accurate rates, potentially reducing the financial impact on them. Additionally, this approach aims to address the high costs associated with liquidation incentives, which can significantly drain protocol resources. By making the auction and update process transparent and competitive, the hope is to create a more equitable and efficient system that reduces unnecessary losses and improves protocol health.

What is the role of building a Zk-roll up?

Initially, the concept of moving towards a rollup-based system, especially in the context of MEV (Miner Extractable Value) mitigation and Oracle updates, seemed unconventional. However, recognizing the centralized nature of existing MEV solutions like Flashbots, MEV Share, or MEV Blocker, it became apparent that a shift was necessary. These solutions, running on central servers, require users to place a significant amount of trust in their operations, which contradicts the decentralized ethos of blockchain technology.

Our early approach to selling Oracle updates similarly relied on a centralized server, creating a system where users had to trust us not only with the execution of updates but also with significant amounts of money due to the collateral requirements for participation. This setup posed substantial trust assumptions, especially in the legitimacy of the auction process for Oracle updates, which lacked transparency and verifiability.

The adoption of a rollup allows for a more trustless interaction, enabling users to move their collateral onto the platform without granting us full control over their funds. Moreover, it provides an on-chain track record of auction participation, winners, and execution of updates, significantly reducing trust assumptions. With rollups, the entire process becomes transparent and verifiable, diminishing the need for users to place undue trust in API3 or the integrity of the auction process. This approach aligns more closely with the decentralized principles of blockchain, ensuring that users can engage with the system with greater confidence and security.

Why Polygon?

We chose Polygon due to our strong relationship and confidence in ZK technology’s future. Our decision was informed by our familiarity with Polygon’s ZK EVMs and the demand for Oracle services within their ecosystem. Compared to other platforms, Polygon’s readiness to market ZK solutions gave it an advantage, guiding our preference for collaboration.

What is going to be the role of API3 tokens in the OEV Network?

In the OEV network, the winning bidder is charged an additional 10% fee on top of their bid, which goes directly to API3. This model is slightly different from the typical MEV scenario where 90% of the extracted value goes back to the user and 10% to the MEV service provider. In the case of API3, the entire bid amount benefits the application, and the added 10% fee is split, with half going to API3 as the auction host and the other half distributed among the Oracle providers.

API3 utilizes its share of the fees for token buybacks and burns, a strategy detailed in the white paper. This approach aims to directly reinvest revenue into the API3 token ecosystem. For example, a contract has been developed to use the collected fees to purchase API3 tokens, provide liquidity on Uniswap, and eventually convert everything back to API3 for burning after a year. This method is designed to enhance the liquidity and value of the API3 token, aligning the interests of the network’s participants with the long-term health and success of the ecosystem.

The game theory relationship in the Order Flow Auction (OFA)?

Essentially, when you conduct a transaction through platforms like MEV Share, your transaction has the potential to create value. This concept is utilized in order flow auctions, where bids can be placed on the value generated by your transaction. Mev searchers can then compensate you with a significant portion of the value extracted from bundling and selling your transaction flow to others. For instance, a large sell order may trigger lucrative liquidations, making your transaction highly valuable to bidders who can exploit these opportunities, leading to substantial cashback for you.

In the context of the OEV network, the focus shifts to Oracle updates rather than typical transactions. We engage in specialized order flow auctions dedicated to these updates. Bidders can vie for the chance to initiate Oracle updates, with the proceeds supporting the application or protocol involved. For example, a scenario where a slight price change triggers a major liquidation could represent a significant profit opportunity, prompting competitive bidding for the right to execute that Oracle update.

This system mirrors the dynamics seen in traditional order flow auctions on the Ethereum mainnet, where liquidations and Oracle updates can generate immense value. However, our approach aims to ensure that the benefits are more evenly distributed. By allowing the market to determine the value of Oracle updates, we create an environment where applications can recapture a larger portion of the incentives typically paid out for liquidations, reducing costs dramatically.

To summarize, Oracle updates can unlock significant value, making it crucial to let the market decide their worth. Currently, Oracle updates occur without this market-driven approach, often leading to missed opportunities for value recapture. Our system aims to change that, potentially allowing DApps to retain a greater share of the incentives they offer.

API3 OEV Network uses a first-party data source model. Why is that?

Both third-party and first-party Oracle models involve data aggregation, but the key difference lies in the source of data and the trust assumptions required. In the third-party Oracle model, like that primarily used by Chainlink (which also incorporates some first-party Oracles), data is sourced indirectly. For example, if I operate a node and claim to obtain my prices from CoinGecko, any data consumer must trust not only CoinGecko but also me as the intermediary. This setup introduces additional layers of trust and potential costs, as each participant in the data provision chain needs to be compensated.

Conversely, first-party Oracles directly involve the data source in the Oracle process. Instead of having an intermediary, data sources like CoinGecko themselves run the Oracle, allowing for verifiable and direct data provision. This method significantly reduces the number of entities a consumer needs to trust and cuts down on the overall cost by removing intermediary fees. It leads to a more efficient, cost-effective, and quickly deployable system across various networks.

This efficiency is crucial, especially considering that running basic data feeds, without engaging in activities like MEV extraction, is generally not profitable. Oracle projects often operate at a loss, subsidizing data feeds with the hope of future token appreciation or seeking external subsidies to cover operational costs. By minimizing the number of parties involved and therefore the operational expenses, first-party Oracles offer a more attractive solution to potential partners, combining efficiency with reduced trust requirements and lower costs.

If the rewards of misbehaving is greater than the service fee and penalty?

We focus on first-party oracles because they’re backed by real businesses, making them accountable for their data. If these oracles misbehave, their actions are recorded on the blockchain, allowing for direct recourse. API3 manages and monitors these data providers to ensure quality standards are met. Providers failing to meet these standards are replaced, ensuring the integrity of the data feed. This approach relies on the trust in these providers but aims to minimize it through transparency and accountability. The goal is to move towards a less trust-dependent system in the future.

How does API3 compete with Chainlink and Pyth? What is the end game?

We’re not primarily focusing on Total Value Secured (TVS) as our core metric, given that TVS doesn’t directly contribute to profitability. The reason is that operating data feeds, regardless of the TVS, is currently not a profitable endeavor — there’s no direct mechanism to recapture value into the token from running data feeds. Whether you’re securing $5 billion or $500 million, the financial outcome remains the same: no profit from the activity itself. This realization has led us to concentrate on the OEV network, which offers a way to bring money back to the projects and actually generate revenue.

The OEV network turns TVS into a meaningful figure by directly tying the security work to potential revenue through liquidations and other mechanisms, moving away from a model built on “hopes and dreams.” Currently, the OEV network operates in conjunction with API3’s data feeds, enabling additional updates and revenue streams. We’re exploring extending the OEV network to other oracles, potentially relieving us from the unprofitable aspects of running data feeds and focusing on the lucrative OEV operations.

On many layer 2 platforms, the underlying foundations subsidize the feeds, meaning the actual costs are often covered by entities like Chainlink Labs or the layer 2’s governing bodies, rather than the projects utilizing the feeds. For the most part, the significant and commonly used data feeds do not generate direct profits for Oracle projects. The shift towards the OEV network represents an innovative approach to creating value in the Oracle space, focusing on profitable interactions rather than solely on securing large amounts of TVS without direct financial return.

What differentiates API3 from Oval? Compared to UMA, what are the advantages?

Clarifying, it’s not entirely accurate to say that oracle services like Chainlink don’t make money. They potentially generate revenue by passing costs onto the networks they operate on, such as Arbitrum, where the Arbitrum Foundation covers these fees. The relationship between the number of projects using the feeds and the revenue generated isn’t direct; often, these services aim to cover operational costs rather than profit significantly.

Expansions into new areas like CCIP suggest a strategy to create value beyond mere price feeds, which might be seen more as brand-building tools or break-even operations rather than profit centers. Governance discussions within these projects often reveal the financial challenges of running oracle services, underscoring the move towards potentially more lucrative solutions.

Oval, built atop oracle and Flashbots infrastructures, showcases one approach to capturing value from oracle updates by pre-selling update rights. However, it’s reliant on centralized components and limited by its operational scope to Ethereum mainnet due to Flashbots’ constraints, illustrating a system with significant dependencies.

API3 proposes a more flexible model, offering comprehensive solutions beyond what Oval achieves. It provides options for utilizing API3’s own data feeds for granular updates or enhancing other oracles with additional granularity through the OEV network. This approach reduces reliance on single networks or solutions, aiming for broader applicability across blockchains without the limitations imposed by dependencies on services like Flashbots.

In essence, API3’s strategy encompasses building on existing oracle frameworks, offering enhanced granularity, and expanding the potential for value capture across multiple blockchains. This multifaceted approach aims to navigate the complexities of oracle services, addressing the limitations of current models while exploring avenues for greater efficiency and broader network applicability.

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