enqAI (ENQAI)

Data Delay

Overview

enqAI is a cryptocurrency project focused on generative artificial intelligence. Its goal is to let people use AI models while allowing independent operators to provide the computing power behind them. The project calls this approach “the People’s AI.” Its token, ENQAI, is an Ethereum-based asset designed for participation in that network. (enqai.gitbook.io)

The idea joins two systems. AI models turn requests into text or audio. A network of computers with graphics processing units, or GPUs, supplies the power needed to run those models. ENQAI is intended to help coordinate the people who operate those computers, the users who send requests, and the token holders who help guide the project. Understanding the distinction between the AI services and the token makes the rest of the ecosystem easier to follow.

History & Team

From noiseGPT to enqAI

The project began as noiseGPT, an AI effort centered on generated speech and other audio tools. Its original token launched in February 2023. The team later adopted the enqAI name as it expanded its focus to include a large language model and a proposed decentralized computing network. The ENQAI token launched on December 9, 2023. The project arranged a one-for-one allocation for holders of the earlier noiseGPT token. (enqai.gitbook.io)

The project’s team page identifies noise and M_RKZ as co-founders of enqAI. It lists John Conquest as chief technology officer and Tommy2.eth as an advisor. These names and roles describe the team presented in the project’s documentation; they also explain how work on noiseGPT carried into the broader enqAI effort. (enqai.gitbook.io)

Technology & How It Works

AI requests and GPU nodes

An AI inference is the work a trained model does when it answers a request. For example, generating a reply to a written prompt is an inference. Running a large model requires computing power, often supplied by GPUs. enqAI’s design calls for multiple node operators to supply that power rather than relying on one central operator. Its documentation describes Eridu as its language-model effort and noiseGPT as its text-to-speech effort. (enqai.gitbook.io)

In the proposed workflow, a user or application sends a request, an eligible node runs the model, and the result returns to the user. The project describes a node-selection method that weighs how much ENQAI a node has staked, its past reliability, and its response time. It also describes limits on the effect of a large stake, so the amount locked by an operator is one factor rather than the only factor in assigning work. (enqai.gitbook.io)

This approach gives the token a role in coordinating computing work. Ethereum records ownership and transfers of ENQAI, while the AI calculations are meant to take place on GPUs. That division matters: storing a token on a blockchain and producing an AI answer are different tasks. The value of the network design depends on how well its node software can connect those tasks in practice.

Tokenomics & Utility

Supply and intended roles

ENQAI uses an Ethereum token contract at 0x710287D1D39DCf62094A83EBB3e736e79400068a. The contract records a maximum supply of 1.1 billion tokens; supply information also accounts for 100 million tokens sent to a burn address. The project describes the original noiseGPT distribution as a full-supply launch on Uniswap, with no reserved team or venture-capital allocation and no vesting contract. (etherscan.io)

The project sets out several jobs for ENQAI. Node operators would stake it to qualify for inference work, then earn rewards for handling requests. Holders could delegate tokens to an operator instead of running a GPU themselves. The documentation also names client discounts, governance, network security, and incentives for node uptime as parts of the economic model. (enqai.gitbook.io)

A further proposal links use of the AI services to the token system. The project describes subscription and business API revenue flowing toward nodes and their delegates, a grant program governed by token holders, and token buybacks. These are described uses within the project’s model, rather than a measure of how much work the network processes. Together, they show how ENQAI is meant to connect demand for AI services with the people supplying computation. (enqai.gitbook.io)

Ecosystem & Use Cases

enqAI’s ecosystem has two main kinds of AI work: language generation and speech generation. A language model can help produce written responses for an application. Text-to-speech turns written words into audio, which can support narration, voice-overs, games, and other creative tools. noiseGPT’s documentation describes access through a web app, an API, and bots for messaging platforms. (enqai.gitbook.io)

For developers, an API offers a way to send requests from another application to an AI service. That makes the system relevant to a team building a chatbot or an audio feature without creating its own model from the ground up. The project’s wider plan is for GPU operators to handle such requests through a network that rewards useful computing work. (enqai.gitbook.io)

The documentation also describes relationships with projects interested in AI-powered applications. For example, it says Lush AI planned to use an enqAI language-model API and work with the team on model fine-tuning for chat. This illustrates one proposed business use: adapting a general model to a particular application. It is distinct from the noiseGPT audio tools, which focus on turning text into speech. (enqai.gitbook.io)

Advantages & Challenges

A distributed pool of GPU operators could let more people take part in providing AI computation. Staking and delegation offer separate roles for computer operators and token holders. The combination of language and speech tools also gives enqAI more than one type of application to develop. These features form the project’s main case for linking an AI service to a token network. (enqai.gitbook.io)

The design brings difficult practical questions as well. Assigning requests across many operators calls for dependable node software, steady response times, and a way to measure whether work was completed well. An open approach to model outputs also leaves decisions about accuracy, data use, and appropriate applications to the people building and using services. These are ongoing challenges for the project’s technology and operating model, rather than properties that a token alone can solve. (enqai.gitbook.io)

Where to Buy & Wallets

ENQAI is available through Uniswap V3 on Ethereum, where it can be swapped against wrapped ether, or WETH. A wallet connected to Ethereum can hold the token and interact with the exchange. Ethereum transactions also require ETH to pay network fees. (coingecko.com)

MetaMask can hold ENQAI as a custom Ethereum token. Its official contract address is 0x710287D1D39DCf62094A83EBB3e736e79400068a. In MetaMask, a holder can select Ethereum and import that address to display the token. The contract address identifies the ENQAI token introduced in the project’s 2023 migration from noiseGPT. (enqai.gitbook.io)

Regulatory & Compliance

ENQAI’s legal treatment depends on the activity and the jurisdiction. In the United States, the Securities and Exchange Commission’s March 2026 interpretation explains how federal securities laws may apply to different crypto assets and to transactions involving them. In the European Union, the Markets in Crypto-Assets framework sets rules for covered offerings and for businesses providing crypto-asset services. The project describes ENQAI as a utility token for its ecosystem, while those broader rules address how tokens are offered and services are operated. (sec.gov)

ENQAI’s halal or Shariah-compliant status has not been established by a project-specific ruling in its published documentation. Its AI purpose alone does not settle that question. Malaysia’s Securities Commission Shariah Advisory Council, in guidance concerning digital assets under its jurisdiction, assesses a digital token by looking at the uses of issuance proceeds and the rights and benefits attached to it. For enqAI, that kind of assessment would include its planned staking, delegation, rewards, and service uses. (sc.com.my)

Future Outlook

The project’s long-term direction is to connect demand for AI tools with a wider group of GPU operators. Its published plans describe work on language models, decentralizing model inference, node selection, staking, and services that outside developers can use. The central measure of progress is how these pieces work together: whether applications send requests, operators complete them, and the token’s incentives support that activity. (enqai.gitbook.io)

enqAI also leaves room for different kinds of applications. Written responses and generated speech can serve separate products or work together in one. As the ecosystem develops, clear information about available models, node participation, and developer access will help show which parts of the design have moved from plans into regular use.

Summary

enqAI grew from noiseGPT’s audio focus into a broader project for decentralized AI. ENQAI is designed to coordinate GPU nodes, reward inference work, support delegation, and give holders a role in governance. Its place in the crypto ecosystem is the link it seeks to build between a transferable Ethereum token and useful AI computation. (enqai.gitbook.io)