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May 12, 2022

Presale Transparency Regarding pENCTR

A fundamental design tenant of the ENCTR protocol that we are using is incentive alignment. This includes making sure that the incentives of the team and early investors are aligned with the rest of the community, and the growth of the protocol as a whole.

Specifically, this means that in order to pay ourselves as a team, we are not going to pre-mint a large amount of $ENCTR and hold it. This would not only break the protocol’s stablecoin backing, but it would also incentivize dumping the $ENCTR as soon as possible to turn a profit.

So instead, we’re going to be using a different token that we are calling pENCTR, a precursor derivative of ENCTR modeled after OlympusDAO’s pOHM.

The pENCTR token gives the holder the option to mint 1 ENCTR by burning 1 pENCTR and depositing 1 DAI into the Treasury. This makes it very similar to an option in that the holder is incentivized to exercise only if the price of ENCTR is above $1.

As an example, if the current market price of $ENCTR is $10, then the value of pENCTR is $9, and it makes sense to exercise. This incentivizes the holder to participate in the community, and assist in building the protocol to increase the price of $ENCTR.

However, there’s a subtle problem.

What happens if the price of $ENCTR climbs very quickly? The holders of pENCTR are incentivized to dump the tokens very quickly to capture those gains, which may negatively impact the price for the rest of the community. To prevent this, we vest the pENCTR based on a percentage of total ENCTR supply. As the Protocol grows and the number of ENCTR tokens increases, the number of exercisable pENCTRs also increases.

As a team, we will be allocating to ourselves [330m pENCTR] which will vest at 7.8% of supply.





Contact Information

For any project related questions:

Luka Antolic-Soban - Chief Executive Officer -

For any partnership, influencer or marketing related questions:

Aaron Nichols - Chief Brand Officer -

Chief Technology Officer

Developer and data scientist in machine learning and open technologies.