Don’t just chase the most popular AI crypto projects; look for those with solid fundamentals. Below is a detailed analysis of Viralmind, including its services, underlying principles, and financial and market insights into the VIRAL token.

Viralmind is building Large Action Models (LAMs) that enhance human-computer interactions in digital environments. Think of LAMs as digital tools capable of performing tasks on computers, websites, and documents just like humans.

Viralmind is creating a decentralized AI training ecosystem, allowing AI agents to be trained in a way that eliminates the inherent biases of centralized AI training models. It provides these agents with highly native and focused datasets for training.

At the core of Viralmind is the VIRAL token, which can be acquired through DEXs or earned by training LAMs on the Viralmind platform.

Viralmind is positioned at the edge of a trillion-dollar AI ecosystem and market. The value of human-trained AI models is estimated at $60 million annually or more.

Viralmind is an open-source, decentralized collective intelligence platform designed to transform AI agents into true human assistants. In short, it creates agents that can operate in any digital environment in a human-like manner. Viralmind’s LAMs are designed to navigate and interact with digital environments using keyboard inputs, mouse movements, and clicks, enabling them to perform a wide range of tasks in gaming, productivity, and creative fields.

To train AI agents, users can use the Trading Gym, which effectively converts on-screen actions into training data. This information is then transformed into detailed trajectories, allowing AI agents to learn and improve over time. Viralmind also introduces a data marketplace where users can trade these datasets to further enhance the system’s overall learning capabilities.

A key innovation by Viralmind is the one-click fine-tuning feature, which allows users to customize models like GPT-4o using small datasets. This approach simplifies AI training, making it accessible to a broad audience, even those without deep technical expertise. The system generates structured .jsonl files that capture human behavior and reasoning, providing high-quality data for model improvement.

Viralmind’s LAMs aim to bridge the gap between Large Language Models (LLMs) and direct computer interactions, replacing outdated OCR-based technologies. Viralmind plans to deploy agents both on-chain and on desktops, aiming for seamless integration into gaming, enterprise software, and blockchain applications.

The platform is powered by its native token, VIRAL, which incentivizes users to provide high-quality training data, participate in competitions, and contribute to the growth of Viralmind’s AI ecosystem.

Viralmind reinvests revenue generated from large models into marketing and development, creating an efficient and self-sustaining economy that rewards contributors and supports long-term platform growth.

Viralmind’s flagship product is VM-1, an LAM that replicates human behavior in digital environments. As an advanced LAM, VM-1 enables AI agents to play games, complete tasks, and navigate complex interfaces with human-like fluidity.

The VM-1 ecosystem will feature two distinct tiers:

1. Open-Source Small Models: Compact and efficient models designed for developers looking to enhance existing pipelines by replacing OCR modules. These serve as plug-and-play extensions for any LLM, boosting functionality without requiring full LAM training.

2. Foundation LAM via API: The large VM-1 model, trained on millions of data points, is suitable for applications ranging from gaming and work automation to streaming. Its usage is powered by the VIRAL token, with fees reinvested into marketing and growth, ensuring ecosystem sustainability.

Viralmind has also established strategic partnerships with game studios, enterprise software providers, and crypto platforms to expand VM-1’s reach. These collaborations will integrate VM-1’s capabilities into broader AI ecosystems, enhancing the adoption and potential of agent frameworks.

• For Gamers: VM-1 agents can seamlessly play alongside users, engaging in cooperative, competitive, or creative gameplay. Users can train their agents with personalized data to master specific games, genres, or strategies.

• For Professionals: VM-1 can automate repetitive manual tasks like form filling and document processing, streamlining workflows in real-world scenarios.

• For Developers: Developers lacking resources to train full LAMs can leverage VM-1’s smaller models to upgrade existing tools and frameworks. Viralmind also allows users to train their own AI agents, bridging the gap between text-based LLMs and real-world computer interactions.

Viralmind has not pursued viral marketing like other projects. It lacks a Discord server but has a Telegram channel with over 1.1K members. The existing community demonstrates a deep understanding of Viralmind’s products. Viralmind is not listed on GoatIndex but is featured on Cookie.fun.

Access to large datasets is fundamental for training AI models. Viralmind is at the heart of this training process, incentivizing user participation and enabling training at scale while maintaining a high degree of personalization.

Centralized AI training models often limit the AI’s ability to understand highly specific user needs and can inherit biases from the organizations or individuals building them. This is where decentralized AI training models like Viralmind come into play. Viralmind is not alone in this space; other projects like FLock.io, Sapien AI, Prime Intellect, DecentrAI, and Prometheus-X are also exploring decentralized AI training.

For instance, FLock.io allows users to train custom, highly focused AI models, with the FLOCK token offering utility similar to VIRAL. Sapien AI focuses on institutional/business AI training, while Prime Intellect brings together researchers, users, and enthusiasts to contribute capital, computation, or code to build models.

The demand for decentralized AI training is evident, with even major LLM projects like those partnering with Reddit for data training deals worth over $60 million annually. This highlights the massive and growing market for AI training models.

While the overall AI market is valued in the trillions, Viralmind occupies a smaller but critical niche—training. Its LAMs will play a key role in shaping how humans interact with AI, particularly AI agents. By 2030, the AI agent market is projected to grow to $47 billion. Even capturing 1% of this market would mean $470 million in revenue. Additionally, the decentralized AI ecosystem, currently valued at $6 billion, is expected to grow rapidly.

At the core of the Viralmind protocol is the VIRAL token, which serves two primary functions:

1. Incentivizing LAM Training: Users earn VIRAL tokens by training models.

2. Staking for Competitions: Users can stake VIRAL tokens to participate in competitions.

In free competitions, users earn rewards from the Training Gym library, distributed to those who complete tasks. In staking competitions, users earn rewards based on their staked amounts, with a 5-10% protocol fee deducted and sent to the treasury.

Additionally, users must hold a certain amount of VIRAL tokens in their wallets to participate in free competitions, adding another layer of utility to the token.

VIRAL Token Details:

• Circulating Supply: 965,888,531

• Max Supply: 1,000,000,000

• Market Cap: $14 million

• Total Holders: 3,000

• Smart Wallet Holders: 5

• KOL/VC Wallet Holders: 22

• Whales: 86

Comparison with AI Market Leader AIXBT:

• Market Cap: $573 million

• Circulating Supply: 855,612,732

• Max Supply: 1,000,000,000

The VIRAL/AIXBT market cap ratio stands at 2.4%, which is considered healthy for a project in its early stages, given its niche in the broader AI ecosystem. Moreover, the sell pressure from non-circulating tokens is only 3-4% of the total, highlighting the strong fundamentals of the VIRAL token and its potential for growth in the coming weeks and months.