API3
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API3

API3 Tokenomics Update

Through the ICO boom of 2017, tokens were mostly seen as a fundraising apparatus, and the word tokenomics was used to refer to quantitative specs, such as the total supply and emission rate of the token. However, tokenomics is more fundamentally about supply and demand, which is shaped by two main factors: token utility and price speculation. A token that has no utility has to rely on the promise of a pump for its survival.

On the other side of the coin, we are increasingly seeing projects wielding their tokenomics as a self-perpetuating tool that optimizes stakeholder incentives to maximally benefit the project. This causes the project tokenomics to strengthen in return, and creates a positive feedback loop. This can be achieved by a good combination of token utilities, and among these, governance is king. By giving true ownership of the project to the token holder, it aligns their incentives with those of the project fully. However, unlocking the entire potential of a governance token necessitates a carefully designed distribution.

In the past month, we have been talking with our investors — who are experts in this kind of high-level design — about how to hone our tokenomics. After these thought provoking discussions, we concluded on three updates that will improve upon the design that we have put forth in our initial whitepaper. Let us go over what these are and what improvements they provide (skip to the Conclusion for the rundown).

1- Discard the front-loaded inflation schedule

Here is the inflation curve from the initial version of our whitepaper:

The inflation schedule to be discarded.

Long story short, front-loaded inflationary rewards entrench founders and private investors — let’s call them innovators for brevity. Innovators have done their due diligence and are in for the long ride; they will not dump, but rather stake continually. Since it’s safe to assume that a smaller ratio of the public will stake at the beginning, this will result in innovators accumulating a larger percentage of the tokens than they have started off with.

From Diffusion of Innovations (1962) by Everett Rogers (source)

The smart innovator doesn’t want this, because it’s ideal for the tokens to be diffused to a wider base of stakeholders for the success of the project — at the end of the day, this will yield a better return for the innovator as well. Diffusion of tokens happen naturally due to the tendency of entropy to increase, but when the inflationary rewards are front-loaded, all innovators will have to stake and accumulate as many tokens as they can to avoid being disproportionately diluted — a typical example of the prisoner’s dilemma.

In short, a front-loaded inflation schedule obstructs the natural diffusion of tokens from innovators to the public, and stagnates the adoption of the project. This brings us to update #1: Discard the current inflation curve.

2- Float inflationary rewards with respect to the total staked amount

Let us consider why we have inflationary staking rewards in the first place. In the absence of revenue, inflationary rewards incentivize token holders to stake at the API3 pool, and thereby secure the governance of the project and act as the collateral for the insurance services. As revenue kicks in, inflationary rewards may be decreased while keeping the total staked amount at the desired level.

Note that the main objective of regulating the inflation rate is to meet a desired level of total staked amount. Then, it would be natural for the DAO to govern this stake target instead, and the inflation rate to float to meet this.

Inflationary rewards converge to the real inflation rate given that there is no insurance risk, and to a proportionally higher value in the presence of insurance risk.

In the graph above, the inflation rate is directly correlated with the difference between the staked amount and the stake target (that the DAO has set), and the inflation rate would be zero if the staked amount was equal to or greater than the stake target. For this reason, we can expect the staked amount to never reach the target, but rather stay at a level that pays out the real inflation rate (note that I’m omitting the insurance risk and other factors for simplicity).

We can expect that, with a constant stake target, the staked amount will converge to the target from below. But what happens when the DAO updates the stake target? Let’s do a transient analysis:

The first big dip after the stake target is decreased is called the overshoot. It can be reduced by updating the stake target in smaller steps, or by updating the inflationary rewards gradually, which is called damping.

Here, the DAO no longer requires as large of a collateral pool (e.g., demand for the insurance services decreased), and thus has reduced the target stake. The inflation rate reacts accordingly, and results in the staked amount to meet the target automatically.

Given that the main objective of paying out inflationary rewards is to maintain a desired staked amount, this scheme not only solves the issue of innovators being entrenched, but it also sets up an elegant framework for governing the inflationary rewards. The API3 DAO decides on a stake target, which is a function of the total value of the project and the amount covered by the insurance services. This is much more intuitive and justifiable compared to governing the inflation rate directly in arbitrary percentages.

For completeness’ sake, let us also discuss the case where the DAO sets an extremely large stake target, which cannot be reasonably met due to the associated insurance risks. For example, the DAO may be looking for a large amount of collateral to insure a decentralized API that obviously cannot be trusted to secure that amount. In this case, the inflation rate will increase with minimal response from the total staked amount, which signals to the DAO that they should first improve the security of their dAPIs through employing more/better first-party oracles, improving their operational processes, etc. As such, this scheme is desirable in that it directly incentivizes the DAO to optimize dAPI security and reliability.

3- Burn the revenue

Until this point (and also in the initial version of our whitepaper), we mentioned two types of staking rewards:

  • Inflationary rewards
  • Revenue distribution

In this context, inflationary rewards is a counterbalance to revenue distribution; it acts as a substitute during the initialization of the network. This can very much be likened to the mining rewards for Bitcoin and Ethereum. Mining a block pays out the block reward (inflation) and the fees of the transactions in the block (revenue). Block rewards are scheduled to decrease over time, and transaction fees will dominate then.

EIP-1559 poses that (among other things) a primarily transaction fee-based miner incentivization scheme causes instability. Instead, it is proposed for the user to pay a base fee that floats with network usage, and this fee to be burned. Then, the miner only gets paid inflationary rewards and the miner bribe, which is whatever the user is willing to pay on top of the base fee.

The parallels

It’s easy to see that an API3 DAO that distributes any significant revenue to its stakers/governing members will suffer from the same instability. Each revenue distribution event creates a discontinuous jump in terms of incentives, which can be abused for profit. For example, a group of stakers may propose dAPI subscription fees to be paid yearly instead of monthly, and cash out as soon as the subscription fees are paid and the revenue is distributed. Therefore, the selfish mining threat mentioned in EIP-1559 arises in this context as selfish governance. Similarly, it would be expected for the total staked amount to oscillate according to periodic subscription fee payments.

Just as the problem is an analog, so is the solution. The API3 DAO should require users to burn API3 tokens to gain access to dAPIs and receive insurance services. Then, stakers/governing members will only receive inflationary rewards as described above in (2). This smooths the benefit a DAO member receives from the DAO revenue, and thus greatly reduces the instability caused by discontinuous rewards. Furthermore, it improves the rewards governance scheme introduced in (2), as the DAO no longer has to consider how revenue distribution will interact with inflationary rewards. There will only be one dial for the DAO to adjust, and it will directly contribute to the DAO’s objective to keep a specific amount of collateral at the staking pool.

In this analogy, the miner bribe is a tool to decide on who gets to use the limited bandwidth. In the API3 DAO’s case, using the bandwidth corresponds to making proposals. We consider this subject to be outside the scope of the current discussion about tokenomics, but it being an important element of the EIP is a good indicator that we should also employ a scheme to limit the members’ ability to make proposals.

The one on the right is better if that one knob controls the right thing, especially in a DAO context.

Conclusion

Instead of hastily implementing the initial design, we published our work for review and received some very insightful feedback. Now, we are picking the fruits of this approach by being able to deploy this iterative improvement before going live. To briefly summarize:

  • We are moving away from a predetermined inflation schedule. Instead, the API3 DAO will set a target staked amount, and the inflationary rewards paid out to stakers will float to meet this target.
  • The API3 DAO revenue will be burned. Paired with the floating inflation rate, this will correspond to the revenue being distributed to the stakers in a much smoother manner, resulting in stability in terms of aligning the governing parties’ incentives with of the DAO’s.

These updates will result in stronger and more robust tokenomics, which as mentioned at the beginning of the post, is critical for the project to fulfill its potential. Going forward, API3 will have the decentralized governance structure to further optimize its tokenomics by implementing new incentive mechanisms.

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