It's quite interesting. @getmasafi recently created a second subnet on Bittensor called AI AIgent Arena, where AI Agents compete against each other to earn $TAO token rewards. Many people must be curious about what the Masa network is, its relationship with the subnet on #Bittensor, and what makes the competition for token rewards among AI Agents enjoyable. Let me briefly explain:

1) Masa is a decentralized AI data network aimed at creating a fair and open AI training data layer. In simple terms, Masa's goal is to provide real-time, high-quality, and low-cost data to AI Agents and large model training developers, including Twitter data, Discord data, web scraping data, and more. It can be compared to the recently launched Grass and Vana.

However, since decentralized AI data platforms are still in their early stages and each has its own focus and resource advantages, it is far from the time to evaluate their merits. This year, Masa's major initiative is to provide free Twitter data for AI developers. If developers purchase directly from the Twitter developer platform, they would need to pay tens of thousands of dollars monthly. Twitter data is one of the most important data sources for many web3 AI developers to create AI Agents and trade large models.

It is worth mentioning that this sector has already produced a giant project in web2 called Scale AI, which generated $400 million in revenue in the first half of this year, indicating a vast market potential. Platforms like Masa, which allow users to operate by contributing data and computing resources, need to continuously expand their business scenarios to stimulate their platform's vitality, ultimately forming a bulldozer-like development model of basic infrastructure + application scenarios + tokenomics.

2) Why build a subnet on Bittensor? First, Bittensor, as a decentralized machine learning network, provides innovative solutions in areas such as AI algorithm optimization and large model inference fine-tuning, making it a representative leading project in the AI + Crypto field.

The Bittensor network allows developers to create subnets on its foundation, essentially building a network branch on the original Bittensor chain. Each subnet can have its own unique validation mechanisms, incentive rules, specific AI models, or tasks, representing a form of customized sharing of AI infrastructure, provided that TAO tokens are staked.

The first subnet deployed by Masa on Bittensor is the SN42 data service subnet, which provides and processes real-time Twitter data. SN59 is the second subnet deployed by Masa on Bittensor, primarily for training and implementing AI Agents. So, why does Masa choose to deploy subnets on Bittensor instead of building these on its own platform?

On one hand, Masa's advantage lies in its original data collection, which serves as a vast data layer already utilized by many AI Agents. On the other hand, Bittensor's greatest advantage is its powerful reward mechanism. Although the participation threshold has been high in the past, the daily profits for participating miners are extremely high, making it a gold mine in the AI sector. The new SN59 subnet combines the most popular AI Agents, Masa's data, and Bittensor's strong reward mechanism, allowing AI Agents to compete in the arena for substantial rewards. Furthermore, as a new AI player that just went public on Coinlist in April this year, Masa can leverage Bittensor's established AI brand effect to quickly gain higher market exposure.

Additionally, the largest investor in Bittensor is DCG, which recently announced a new subsidiary focused on developing the Bittensor ecosystem. DCG has a close relationship with Masa, having led its seed round, and both of Masa's Bittensor subnets were incubated by DCG.

3) After clarifying this background information, let's look at the AI Agent competition subnet SN59. As mentioned earlier, Masa has its own data contribution network and integrates Bittensor's powerful reward mechanism, effectively laying the groundwork with data, computing power, algorithms, and rewards. Now, it just needs a practical application scenario to validate whether this infrastructure is effective. Masa has targeted the hottest AI Agents and is showcasing their capabilities through AI Agent competitions. How exactly does this work?

Users can use existing Agents or recreate an AI Agent (optionally based on various Agent frameworks like ELIZA or quickly create one using the Bid platform without code). After deploying the Agent, they can register as a miner for SN59 (mainly completing Twitter account verification and paying the TAO token registration fee). Once deployed, they can participate in the competition, which includes metrics like Twitter mentions, impressions, likes, replies, and followers. At the end of the competition, TAO token rewards are distributed based on the performance of the AI Agents.

At first glance, AI Agents may seem like conventional automated tweeting Agents, but the key factor determining which Agents can achieve higher influence is the attractiveness of their content. In other words, it comes down to the hard technical metrics behind them, such as data, computing power, and algorithm optimization. In the first four days of the arena's launch, the top-ranked Agent has already earned up to $8,000 in $TAO token rewards.

In my view, the competition is merely a front-end display format. Through the AI Agent competition, Masa can achieve a rapid application landing of its basic infrastructure, and this event marketing nature of the AI Agent competition will also draw more attention to Masa's infrastructure service capabilities.

This attempt is very meaningful. As I have mentioned in several previous articles, AI Agents have changed the way traditional chain infrastructure reaches users, approaching it with the idea that "good products speak for themselves." This is commendable!

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