# Burn & Deflation Model

#### 🔥 Core Deflation Principles

1. **All AI features require PUAI to activate**
   * Data requests, content generation, model calls, Telegram plugin actions
   * Each interaction priced transparently on a per-feature basis
2. **100% of consumed PUAI is instantly burned**
   * No token recycling or treasury redirection
   * Burn address is published and fully verifiable on-chain
3. **The more it's used, the more it burns**
   * Higher usage → faster token flow → higher burn pressure
   * Larger communities = more bot use = greater demand for PUAI

***

#### 📈 Utility-to-Value Framework

| Module                 | PUAI Usage                                    | End-User Value                               |
| ---------------------- | --------------------------------------------- | -------------------------------------------- |
| Telegram Bot Tools     | Tasks, points, leaderboard, user segmentation | Save time & increase community effectiveness |
| AI Data Insights       | Market trend reports, user modeling           | Improve strategy & targeting accuracy        |
| Smart Contract Parsing | Risk alerts, structure visualizers            | Enhance blockchain operation security        |
| Content Engine         | Tweet generation, image AI, translation       | Reduce creative workload & amplify reach     |

***

#### 🔄 Behavior Loop & Scarcity Model

* **Users:** Project teams, community managers, KOLs
* **Token Flow:** Usage → Consumption → Burning → Supply Drop → Scarcity → Value Consolidation
* **No Inflation, No Passive Holding** — only demand-driven utility


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://pumpad.gitbook.io/pumpad.ai/burn-and-deflation-model.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.