> For the complete documentation index, see [llms.txt](https://docs.flock.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flock.io/flock-tokenomics/network-participation/fomo/usdmt-supporters.md).
# $MT Supporters
$MT Supporters are participants who buy, hold, stake, or otherwise support a model token.
They may be model users, investors, community members, distributors, or ecosystem participants who believe a specific model deployment will gain usage.
#### Why $MT Supporters Matter
$MT Supporters help bootstrap the model economy. They provide initial demand during the launch phase, help form liquidity, and may stake tokens to strengthen the model’s incentive base.
Unlike purely speculative assets, $MT is designed to be tied to model usage. As the model generates revenue, a portion of net revenue is used to buy back and burn the relevant $MT. This creates direct linkage between inference demand and model-token scarcity.
#### Ways $MT Supporters Earn
$MT Supporters can earn through several channels:
| Channel | Description |
| ------------------------- | ------------------------------------------------------------- |
| $FLOCK global emission | Distributed to active $MT economies |
| Per-$MT $FLOCK incentive | Dedicated $FLOCK incentive for a specific model token |
| Per-$MT token incentive | Rewards paid in the model’s own token |
| Staking advantage | Higher reward score than non-stakers at equal usage |
| Buyback and burn exposure | Potential appreciation from model revenue reducing $MT supply |
#### Staking and Usage
The system is designed so that $MT staking improves reward share, but staking alone should not dominate.
A participant’s score depends on:
```
usage share × staking factor × gmFLOCK multiplier
```
This means the strongest participants are not merely the largest holders, but those who combine:
```
real API usage + aligned $MT staking + ecosystem-level gmFLOCK commitment
```
#### Whale Resistance
Both the staking factor and gmFLOCK multiplier are designed with non-linear effects.
The staking factor can use a square-root structure, meaning the benefit of staking increases with stake share but at a decreasing rate. gmFLOCK also uses a diminishing-return structure, where larger holdings move users closer to the upper multiplier range but do not scale infinitely.
This keeps the reward system competitive.
A whale can improve their position, but they still need real usage. Meanwhile, smaller users can remain competitive if they generate meaningful API demand in a specific $MT economy.
---
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