Author: Teng Yan, Chain of Thought

Translation: Golden Finance xiaozou

In 2021, I was still an Axie Infinity player and ran a small scholarship guild. If you haven't experienced that era, let me tell you - it was absolutely wild.

Axie Infinity has made people realize that Cryptocurrency and games can be combined. Essentially, this is a simple Pokémon-style strategy game, where players need to build a team of 3 Axies (fierce warriors), each with unique abilities. You can lead your team to battle other teams, participate in the game, and win SLP Token rewards.

But what really excites non-gamers is the potential to make money through gaming. Axie's rapid rise is due to two major mechanisms:

The first one is Breeding Axies. Get two Axies, use SLP Token to breed them, and voilà—a new Axie with unique abilities combining the original two Axies is born. As a result, these rare and powerful Axies (referred to as OP Axies by gamers) have become popular commodities, and a busy breeding market has emerged.

The second mechanism is the scholarship program. Business players from all over the world began to lend Axies to 'scholars'. These players are usually from developing countries such as the Philippines or Argentina, where they cannot afford the initial cost of more than $1,000 to buy three Axie Non-fungible Tokens. Scholars play games every day to earn tokens and share profits with scholarship guilds, which usually take a 30-50% cut.

During its heyday, especially during the 2019 epidemic, Axie had a significant impact on the local economies of developing countries. In the Philippines (where about 40% of Axie Infinity users are located), many players earn incomes far above the minimum wage. Guilds make substantial profits.

This solves a key problem for game developers: player Liquidity. By incentivizing players to actively play games for a few hours every day, Axie ensures that every player will have an opponent waiting for them, making the gaming experience more engaging.

But this comes at a cost.

To address the liquidity issue for players, Axie gave out a large amount of Tokens to incentivize player participation. The story begins here. Due to the unlimited supply of SLP, the Token experienced a crazy inflation and a big dump in price, causing the ecosystem to collapse. When the Token depreciates, players will leave. Axie went from being a darling of 'play-to-earn' overnight to a cautionary tale.

But what if there is a way to solve the players Liquidity problem without the need for unsustainable Token economics?

This is exactly what ARC / AI Arena has been quietly working on for the past three years. Now, it is beginning to bear fruit.

1, Player Liquidity is the lifeblood

Players' Liquidity is the lifeblood of multiplayer games and also the key to long-term success.

Many Web3 and indie games are facing the 'cold start' problem - too few players to quickly match or form a thriving community. They lack the marketing budget or natural IP awareness that big game companies have. This can lead to long wait times, inability to match, and high churn rates.

These games usually die out slowly and painfully.

Therefore, game developers must prioritize player Liquidity from the beginning. Games require various activities to maintain fun - chess requires two players, while large-scale battles require thousands of players. Skill matching mechanisms further raise the bar, requiring more players to maintain fairness and attractiveness of the game.

For Web3 games, the risks are greater. According to Delphi Digital's annual gaming report, the user acquisition cost for Web3 games is 77% higher than that of traditional mobile games, making player retention rate crucial.

A strong player base can ensure fair matches, a vibrant game economy (i.e. more item trading), and more active social interactions, making games more fun.

2, ARC——AI Game Pioneer

ARC developed by ArenaX Labs is leading the future of AI-powered online gaming experiences. In short, they use AI to solve the Liquidity problem that troubles new game players.

The problem with most AI robots in games today is that they are too poor. Once you spend a few hours mastering the tricks, these robots become very easy to defeat. They are designed to help new players, but they cannot bring too many challenges or stickiness to experienced players.

Imagine the skills of AI players can rival top human players. Imagine being able to compete against them at any time, anywhere, without waiting for a match. Imagine training your AI player to mimic your gaming style, own it, and earn rewards through its performance.

This is a win-win situation for both players and game companies.

Gaming companies use human-like AI robots to make games popular, increase player Liquidity, improve user experience, and increase retention rates - these are key factors for the survival of new game latecomers in a competitive market.

Players have gained a new way to participate in the game, establishing a stronger sense of belonging by training AI and competing against it.

Let's see how they do it.

3. Product and Architecture

The parent company ArenaX Labs is developing a series of products to solve the Liquidity problem for players.

• Existing product: AI Arena, an AI fighting game.
• New Product: ARC B2B, an AI-driven game SDK that can be easily integrated into any game.
• New Product: ARC Reinforcement Learning (RL)

（1）AI Arena: Game

AI Arena is a fighting game that reminds people of Nintendo's Super Smash Bros, with all kinds of quirky cartoon characters battling in the arena.

But in AI Arena, each character is controlled by AI - you are not playing as the warrior, but as their coach. Your mission is to train your AI warriors using your strategies and expertise.

Training your warriors is like training a student for battle. In training mode, you open data collection and create combat scenarios to fine-tune their actions. For example, if your warrior is close to the opponent, you can teach them to block with your shield and then follow up with a combo. How about long-range combat? Train them to launch ranged attacks.

You can control what kind of data to collect, ensuring that only the best actions are recorded for training. With practice, you can refine hyperparameters to gain more technical advantages or simply use beginner-friendly default settings. Once the training is complete, your AI warrior can join the battle.

The beginning is always the hardest - training an effective model takes time and experimentation. My first fighter fell off the platform several times, not because it was hit by an opponent. But after a few iterations, I successfully created a well-performing model. It is truly satisfying to see your training paying off.

AI Arena has introduced additional Depth through Non-fungible Token warriors. Each Non-fungible Token character has unique visual features and combat attributes that will affect gameplay. This adds another layer of strategy.

Currently, AI Arena is running on the Arbitrum Mainnet, and only those who have AI Arena Non-fungible Tokens can access it, while maintaining the exclusivity of the community while improving the gameplay. Players can join guilds, gather champion Non-fungible Tokens and NRNs for on-chain battle rankings and rewards. This is done to attract loyal players and promote competition.

In the end, AI Arena is the showcase of ARC's AI training technology. Although this is their entry point into the ecosystem, the real vision goes far beyond the game itself.

(2) ARC: Infrastructure

ARC is an AI infrastructure solution designed for game design.

The ArenaX team started from scratch, even developing their own game infrastructure, because existing solutions such as Unity and Unreal could not meet their vision.

In more than three years, they have carefully designed a powerful technology stack that can handle data aggregation, model training, and model inspection for imitation and reinforcement learning. This infrastructure is the backbone of AI Arena, but its potential is much greater.

As the team continues to refine their technology, third-party studios are starting to discover ARC and hoping to obtain authorization or white labels for the platform. Recognizing this demand, they formalized ARC's infrastructure into a B2B product.

Now, ARC directly cooperates with game companies to provide AI game experiences. Its value proposition is:

• Permanent player Liquidity as a Service
• AI gameplay as a simple integration

Permanent Player Liquidity as a Service

ARC focuses on human behavior cloning - training specialized AI models to mimic human behavior. This is different from the primary usage of AI in games today, which uses generative models to create game assets and employs LLM to drive conversations.

With the ARC SDK, developers can create human-like AI agents and expand them according to game requirements. The SDK simplifies the heavy workload. Game companies can introduce AI without dealing with complex machine learning.

After integration, deploying AI models only requires one line of code, and ARC is responsible for infrastructure, data processing, training, and backend deployment.

ARC adopts a collaborative approach with gaming companies to help them:

• Capture original gameplay data and transform it into meaningful datasets for AI training.
• Determine key gameplay variables and decision points related to the game mechanism.
• Map AI model output to in-game activities to ensure smooth functionality - for example, linking AI's 'right-click' output to specific game controls.

How does AI work?

ARC uses four models for game interaction:

• Feedforward Neural Network: Suitable for continuous environments with numerical features such as speed or position.
• Table agent: particularly ideal for games with limited discrete scenarios.
• Hierarchical and convolutional neural networks are under development.

There are two interactive spaces related to ARC's AI models:

The state space defines the agent's understanding of the game at any given moment. For a feedforward network, this is a combination of input features such as player speed or position. For tabular agents, this is the set of discrete scenarios the agent may encounter in the game.

Action space describes what an agent can do in a game, from discrete inputs (like pressing buttons) to continuous control (like moving a joystick). This maps to the game's input.

The state space provides input for the AI model with ARC, and the AI model processes the input and generates output. Then these outputs are transformed into game actions through the action space.

ARC works closely with game developers to identify the most critical features and design the state space accordingly. They also test various model configurations and sizes to balance intelligence and speed, ensuring smooth and engaging gameplay.

According to the team, Web3 companies have particularly high demand for their player Liquidity services. These companies pay for better player Liquidity, and ARC will use a large portion of this revenue for NRN Token repurchase.

Bringing AI Gameplay to Players: Trainer Platform

ARC SDK also allows web3 companies to access their game's trainer platform, allowing players to train and submit agents.

Like AI Arena, players can set up simulations to obtain gameplay data and train blank AI models. These models will evolve over time, incorporating new gameplay data while retaining previous knowledge, without the need to start from scratch with each update.

This opens up exciting possibilities: players can sell their custom-trained AI agents on the market, creating a new in-game economy layer. In AI Arena, skilled trainers can form guilds and offer training services to other companies.

For companies that fully integrate agent functionality, the concept of Parallel Play becomes vivid. AI agents are available 24/7 and can simultaneously participate in multiple matches or game instances. This solves the Liquidity problem for players and creates new opportunities for user stickiness and revenue.

But that's not all...

(3) ARC RL: From one-to-one to many-to-one

If AI Arena and ARC Trainer Platform feel like single player mode (where you can train your own AI models), then ARC RL is similar to multiplayer mode.

Imagine this: a whole game DAO gathering gameplay data to train a shared AI model, which everyone collectively owns and benefits from. These 'main agents' represent the collective wisdom of all players, changing esports through the introduction of collective effort and strategic cooperation-driven competition.

ARC RL uses reinforcement learning (RL) and crowdsourced human gameplay data to train these 'super-intelligent' agents.

The working principle of reinforcement learning is to reward the optimal behavior of the agent. It is particularly effective in games because the reward function is explicit and objective, such as the damage caused, the coins obtained, or the victory.

This is precedent:

DeepMind's AlphaGo defeated professional human players in the game of Go, improving its strategy with each iteration through millions of self-generated training matches.

I didn't realize this before, but long before chatGPT was created, OpenAI was already well known in the gaming community.

OpenAI Five crushed top human players using reinforcement learning in Dota 2 and defeated the world champion in 2019. It mastered advanced strategies such as teamwork by accelerating simulations and utilizing a large amount of computational resources.

OpenAI Five runs millions of games every day, equivalent to 250 years of simulated games per day, powered by 256 GPUs and 128,000 CPUs. By skipping graphics rendering, it greatly accelerates the learning speed.

Initially, the AI exhibited unstable behavior, such as aimlessly wandering, but it quickly improved. It mastered some basic strategies, such as crawling on small paths and stealing resources, eventually evolving into complex operations like ambushes.

The key concept of reinforcement learning is that AI agents learn how to achieve success through experience rather than being directly instructed on what to do.

ARC RL stands out by using offline reinforcement learning. The AI agent learns not from its own trial and error, but from the experiences of others. It's like a student watching videos of others riding bicycles, observing their successes and failures, and using this knowledge to avoid falling and improve faster.

This approach offers an additional benefit: collaborative training and shared ownership of the model. This not only makes powerful AI agents more popular, but also aligns the motivations of players, guilds, and developers.

In the creation of the 'Super Intelligence' game agent, there are two key roles:

• Sponsor: Similar to the leader of a guild, they stake a large amount of NRN Tokens to initiate and manage RL agents. Sponsors can be any entity, but are likely to be gaming guilds, DAOs, web3 communities, or even popular on-chain personalized agents like LUNA.
• Player: stake a small amount of NRN Token to contribute their gameplay data to train the agent's individual.

Sponsors coordinate and guide their player teams to ensure high-quality training data, enabling their AI agents to have a competitive advantage in agent matches.

Rewards are distributed based on the performance of the super agents in the competition. 70% of the rewards belong to the players, 10% belong to the sponsors, and the remaining 20% belong to the NRN Treasury. This structure provides a consistent incentive mechanism for all participants.

Data Contribution

How do you make players willing to contribute their gameplay data? Not easy.

ARC makes it simple and beneficial to provide gameplay data. Players don't need specialized knowledge, just play the game. At the end of a session, they are prompted to submit data to train a specific agent. The dashboard tracks their contributions and the agents they support.

ARC's Attribution Algorithm ensures quality by evaluating contributions and rewarding high-quality, influential data.

Interestingly, even if you are a bad player (like me), your data is still useful. Poor gameplay can help agents learn what not to do, while technically advanced gameplay can teach the best strategies. Redundant data is filtered out to maintain quality.

In short, ARC RL is designed to be a low-friction mass market product centered around agents with capabilities surpassing those of humans.

4. Market Size

ARC's technology platform is multifunctional, supporting various types of games such as shooting games, fighting games, social casinos, racing, trading card games, and RPGs. It is tailored for games that need to maintain player stickiness.

ARC's products mainly target two markets:

ARC mainly follows independent developers and companies, not traditional big companies. Due to limited brand influence and distribution resources, these small companies usually find it difficult to attract players in the early stages.

ARC's AI agent solves this problem by creating a dynamic game environment from the very beginning, ensuring dynamic gameplay even in the initial stages of the game.

This may come as a surprise to many, but the indie game sector is indeed a major force in the gaming market:

• In 2024, indie games accounted for 48% of the total revenue on Steam.

Another target market is Web3 games. Most Web3 games are developed by emerging companies, which also face various unique challenges such as Wallet login, encryption questioning, and high user acquisition costs. These games often have Liquidity issues, and AI agents can fill the gap to maintain the attractiveness of the game.

Although Web3 games have recently struggled due to a lack of engaging experiences, signs of recovery are emerging.

For example, Off the Grid, one of the earliest AAA-level Web3 games, has recently achieved early mainstream success, with 9 million wallets conducting 100 million transactions in the first month. This paves the way for the industry to achieve widespread success and creates an opportunity for ARC to support this revival.

5. ARC Team

The founding team behind ArenaX Labs has rich expertise in machine learning and investment management.

CEO and Chief Technology Officer Brandon Da Silva previously led machine learning research at a Canadian investment firm, focusing on reinforcement learning, Bayesian Depth learning, and model adaptability. He pioneered a $1 billion quantitative trading strategy centered on risk parity and multi-asset portfolio management.

Chief Operating Officer Wei Xie manages a $7 billion Liquidity investment portfolio at the same company and oversees its innovative investment projects, focusing on emerging areas such as AI, machine learning, and Web3 technology.

ArenaX Labs raised a $5 million seed round in 2021, led by Paradigm and joined by Framework Ventures. The company raised $6 million in January 2024, with lead investment from SevenX Ventures, FunPlus / Xterio, and Moore Strategic Ventures.

6, NRN Token tokenomics - a healthy reform

ARC/AI Arena has a Token - NRN. Let's take stock of the current situation first.

Studying the supply side and demand side will give us a clearer understanding of the trend.

（1）Supply Side

The total supply of NRN is 10 billion, with approximately 4.09 billion (40.9%) in circulation.

At the time of writing, the price of this Token is $0.72, which means the market capitalization is $29 million and the fully diluted valuation is $71 million.

NRN was released on June 24, 2024, with 40.9% of the circulating supply coming from:

• Community Airdrop (8% of the total)
• Foundation Treasury (accounts for 10.9%, of which 2.9% is unlocked, linearly unlocked for 36 months)
• Community ecosystem rewards (30%)

The majority of the circulating supply (30% out of 40.9%) is composed of Token rewards from the community ecosystem. The project manages these Tokens and strategically allocates them to stake rewards, game rewards, ecosystem rise programs, and community-driven programs.

The unlocking schedule is reassuring, with no major events in the short term:

• The next unlock is the foundation's OTC sales (1.1%), starting from December 2024, with a linear unlock over 12 months. This will only increase the monthly inflation rate by 0.09%, which is unlikely to cause significant concerns.
• The allocation to investors and contributors (50% of the total supply) will not begin unlocking until June 2025, and even then, it will be unlocked linearly over 24 months.

Currently, the dumping pressure is expected to remain quite manageable, mainly due to ecosystem rewards. The key is to trust that the team has the ability to strategically deploy these funds to drive the rise of the protocol.

（2）Demand Side

NRN v1-Player Economy

Initially, NRN was designed to be a strategic resource associated with the game economy of AI Arena.

Players stake NRN on AI players, and if they win, they will receive rewards, but if they lose, they will lose part of the stake. This creates a direct incentive dynamic, turning it into a competitive sport and providing economic incentives for skilled players.

Rewards are distributed using the ELO system to ensure skill-based balanced payments. Other sources of income include game item purchases, decoration upgrades, and tournament entry fees.

The initial Token model relies entirely on the success of the game and the continuous willingness of new players to purchase NRN and Non-fungible Tokens to participate in the game.

Let's talk about why we are so excited below...

NRN v2 - Player & Platform Economy

The improved v2 tokenomics of NRN expands the utility of tokens from AI Arena to the broader ARC platform, introducing powerful new demand-driven factors. This evolution transforms NRN from a specific game token into a platform token. In my opinion, this is a very positive transformation.

The three new driving factors of NRN include:

Revenue from ARC integration. Game companies integrating ARC will generate revenue for the treasury through integration fees and ongoing royalties tied to game performance. Treasury funds can be used to drive NRN buybacks, develop the ecosystem, and incentivize players on the Trainer platform.

Trainer market fee. NRN obtains value from the fees in the trainer field, and players can trade AI models and game play data on the trainer market.

Participate in the stake of ARC RL: both sponsors and players must stake NRN to join ARC RL. As more and more players enter ARC RL, the demand for NRN also increases accordingly.

What is particularly exciting is the revenue of the game company. This marks a shift from a pure B2C model to a mixed B2C and B2B model, creating a sustained external capital inflow for the NRN economy. As ARC has a broader target market, this revenue stream will exceed the revenue that AI Arena itself can generate.

Although there is potential in the trainer market, the cost depends on whether the ecosystem can reach a critical mass - enough games, trainers, and players to sustain active trading. This is a long-term endeavor.

In the short term, ARC RL stake may be the most direct and reflexive demand-driving factor. Adequate initial reward pool and the excitement of new product releases may trigger early adoption, driving up Token prices and attracting participants. This creates a feedback loop of demand rise and economic rise. However, conversely, if ARC RL struggles to maintain user stickiness, demand may quickly disappear.

The potential of network effects is huge: more games → more players → more games joining → more players. This virtuous cycle can position NRN as the core Token in the Crypto AI game ecosystem.

7, Mother of Game AI Models

What is the ending? The advantage of ARC is that it can promote various types of games. Over time, it enables them to collect a unique specific game play database. As ARC integrates with more games, it can continuously feed back this data into its own ecosystem, creating a rise and perfect virtuous cycle.

Once this cross-sectional game dataset reaches critical mass, it will become a highly valuable resource. Imagine using it to train a general AI model for game development, opening up new possibilities for large-scale design, testing, and optimization of games.

It's still too early, but in the AI era where data is the new oil, the potential in this area is limitless.

8. Our Ideas

NRN evolves into platform game - Token repricing

With the issuance of ARC and ARC RL, the project is no longer just a game company with a single product, it now positions itself as a platform and AI game. This transformation is expected to result in the reevaluation of NRN Token, which was previously limited by the success of AI Arena. The introduction of new Token sources through ARC RL, combined with the external demand for revenue sharing protocols with game companies and trainer transaction fees, creates a broader and more diverse foundation for the utility and value of NRN.

Closely related to the success of game partners

ARC's business model ties its success to the companies it partners with, as revenue streams are based on Token distribution (in Web3 games) and game royalties payment. The closely integrated games are worth a look.

If the ARC game is a huge success, the value generated will flow back to NRN holder. Conversely, if the cooperative game encounters difficulties, the flow of value will be limited.

Looking forward to more integration with Web3 games

The ARC platform is perfect for Web3 games, where competitive gameplay with incentives is perfectly integrated with the existing Token economy.

By integrating ARC, Web3 games can immediately enter the "AI agent" narrative. ARC RL brings the community together and motivates them to move towards a common goal. This also opens up new opportunities for innovative mechanisms, such as making activities such as "game-to-Airdrop" more attractive to players. By combining AI and token incentives, ARC increases the depth and excitement that traditional games cannot replicate.

AI gameplay has a learning curve

AI gameplay has a steep learning curve, which can create friction for new players. It took me an hour to figure out how to properly train my players in AI Arena.

However, the player experience of ARC RL has less friction, because AI training is done in the backend when players play games and submit data. Another pending issue is how players feel when they know their opponent is AI. Does it affect them? Will it enhance or weaken the gaming experience? Only time will tell us the answer.

9. Bright Future

AI will usher in a brand new breakthrough experience in the gaming world.

Teams like Parallel Colony and Virtuals are driving the development of autonomous AI agents, while ARC is carving out its niche market by focusing on human behavior cloning - providing an innovative approach to address player Liquidity challenges without relying on unsustainable tokenomics.

The transition from a game to a mature platform is a huge leap for ARC. This not only opens up greater opportunities through cooperation with game companies, but also restructures the integration of AI and games.

With its improved tokenomics and the potential of strong network effects, ARC's bright future seems to have just begun.