ABSTRACT
The current Oracle Integrity Staking (OIS) system focuses on incentivizing data publishers and other stakers (delegators) to hold and stake $PYTH. However, this system does not directly apply or appeal to another key stakeholder group—data consumers (users of Pyth price feeds).
I am therefore proposing an additional staking program that incentivizes data consumers to hold and stake $PYTH, by offering them discounts on price update fees (when new fee structures are implemented in future). The purpose of this is to give $PYTH added utility, and therefore be an additional driver of $PYTH token price, which I believe is a key factor in Pyth’s future growth.
RATIONALE
First, why is the price of $PYTH important?
Currently, data publishers are rewarded for providing reliable, high-quality data to the network. The primary benefit they receive are publisher rewards in the form of $PYTH (via publisher programs such as the OIS). The attractiveness of this incentive is tied to the $PYTH price. One of Pyth’s main distinguishing features is that it offers first-party data from some of the most established and reputable financial institutions. It is imperative that Pyth maintains this key competitive advantage by ensuring that these data publishers are retained and remain incentivized.
A higher token price means increased value of rewards for data publishers, which in turn retains and attracts more top-tier data publishers. This leads to a higher-quality product, which in turn attracts more data consumers, which equates to more potential revenue, and more value pouring into the ecosystem.
The implementation of a rank-based fee discount system for data consumers would increase buying pressure and therefore bolster token price, leading to the reinforcing cycle of value, as described above.
PROPOSED PLAN AND FEASIBILITY
The system would not be implemented immediately, as I believe fees should still be kept to a minimum (as it is now) to encourage continued growth of Pyth’s market share. Therefore, this will only be implemented once there are significant changes to the update fee structure (i.e. when fees are no longer dirt cheap and discounts actually make a difference).
However, at a strategic time, this new system (if approved) may be communicated to data consumers ahead of time, so that they can start to position themselves appropriately (e.g. start accumulating $PYTH).
Here is a more detailed explanation of how the system could work:
Broad Concept:
- Data consumers are ranked based on the amount and duration of staked $PYTH.
- Fee discounts are awarded dynamically based on these rankings, creating a competitive environment among data consumers to secure the highest possible discount.
- Data consumers with longer staking durations and larger amounts of staked $PYTH will rank higher, incentivizing early acquisition and long-term staking.
- A dynamic ranking system discourages selling or reducing stake, as it would negatively impact their rank and future fee discounts.
Mechanism and Structure:
- The proposed system could be structured under a newly created “Pyth Data Consumer Association” (PDCA), where membership and eligibility to price feed discounts starts with a minimum stake of $PYTH.
- Members of the PDCA will receive different fee discounts based on their staking rank within the PDCA.
- Both the duration of staking and the total amount of $PYTH staked are considered, which attracts and rewards early adopters.
Mitigating Drawbacks:
- To maximize fairness, the system must be designed to protect smaller members of the PDCA, preventing large consumers from monopolizing the system.
- The system must be designed such that it remains attractive to new data consumers in the future. In other words, early adopters should not have an overwhelmingly unbalanced advantage in the system that completely disincentivizes new participants from joining the PDCA in future.
- To encourage long-term holding and continued demand for $PYTH, this system may also be paired with other forms of token utility, such as revenue sharing for token holders (which would further reduce net operating costs for data consumers who are staking $PYTH).
PROJECTED IMPACT ON THE PYTH ECOSYSTEM
The implementation of this rank-based fee discount system is expected to generate several positive effects for the Pyth ecosystem:
Increased Demand for $PYTH:
- Data consumers will accumulate more $PYTH to improve their rank and secure higher fee discounts.
- The staking requirements and competitive nature of the rank system create buy pressure, leading to a net positive impact on token price.
Enhanced Data Publisher Incentives:
- As the token price increases, data publisher rewards have increased value, attracting and retaining top-quality, first-party data providers.
- This further enhances the quality of Pyth’s price feeds and reputation, making it a preferred choice for builders and creating a virtuous cycle of value.
Strengthened Long-Term Value Proposition:
- Data consumers who stake early will be better positioned for higher discounts as network fees increase in the future, promoting a forward-looking mindset that encourages early adoption and long-term participation.
- When data consumers are also $PYTH holders, they have a vested interest in the continued success of the Pyth Network and also reap the benefits when token price goes up. In this way, the interests of stakeholders are aligned.
Broader Ecosystem Benefits
- Regular Pyth community members and stakers benefit from the increased demand and rising value of $PYTH. This in turn supports community growth and awareness in the wider Web3 community.
- If this system is successful, it would contribute to both customer retention and acquisition.
- The proposed system supports sustainable growth and value inflows into the ecosystem, while minimizing the risk of unsustainable price bubbles.
IMPLEMENTATION AND NEXT STEPS
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Design the Ranking Algorithm. Develop a fair and transparent algorithm that considers both staking duration and the amount of $PYTH staked to determine rank and fee discounts. It should be fair and attractive to all potential data consumers, big and small, current and future.
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Obtain DAO approval. Gather feedback from DAO members and the community, refine the proposal and obtain DAO approval.
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Establish the PDCA. Create the Pyth Data Consumer Association as a structured entity within the ecosystem, setting the minimum staking requirements for entry.
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Keep fees minimal in the near-term. To reiterate the point, price update fees should be kept at a minimum to encourage continued growth and adoption. However, there should be a communication plan which encourages protocols to be forward-thinking and consider the potential future benefits of $PYTH staking when new fee structures are eventually implemented.
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Monitor and Adjust. Regularly review the system’s performance, obtaining feedback and adjusting parameters as necessary to ensure fairness, competitive dynamics, and sustainable value generation.
QUESTIONS AND UNCERTAINTIES
The attractiveness of this program (to data consumers) is dependent and proportional to the update fees. Since I expect update fees to remain highly competitive for the near future, the idea of discounts may not sound appealing initially. One possible idea to generate initial momentum and attract data consumers to the PDCA is to offer incentives like premium services, faster speeds, or staking APR.
However, price update fees will inevitably have to go up at some point in order to make Pyth a profitable and sustainable endeavor. Furthermore, incentivization for data publishers must continue even when token emissions cease. When fees go up, customer retention would depend on the extent to which the quality and scope of Pyth’s services surpasses that of its competitors, and the effectiveness of other customer retention initiatives.
This proposed implementation could be a useful piece in addressing some of these uncertainties and helping us realize the bright future we all envision for Pyth.
