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 didn’t live through that era, let me tell you – it was absolutely wild.

Axie Infinity is a game that makes people realize that cryptocurrencies and games can be combined. At its heart, it's a simple Pokémon-style strategy game in which players assemble a team of three Axie (very ferocious warriors), each with unique abilities. You can lead your own team against other teams and receive SLP token rewards by participating in the game and winning.

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

The first is Breeding Axies. Get two Axies, use SLP tokens to breed them, and voilà is born - a new Axie that combines the unique abilities of the two original Axies. As a result, such rare and powerful Axies (called OP Axies by gamers) became a hot commodity, and a busy breeding market emerged.

The second mechanism is the scholarship program. Corporate players from around the world began lending Axies to "scholars." These players are usually from developing countries such as the Philippines or Argentina, and they cannot afford the upfront fee of more than $1,000 to purchase 3 Axie NFTs. Scholars play games every day to earn tokens and share the profits with the scholarship guild, which usually takes a 30-50% cut.

In its heyday, and especially during the 2019 pandemic, 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 well above the minimum wage. The guild made huge profits.

This solves a key problem for game developers: player mobility. By incentivizing players to spend several hours a day actively playing the game, Axie ensures that every player will have an opponent waiting there, making the player experience more engaging.

But this comes at a cost.

In order to solve the liquidity problem of players, Axie sends out a large number of tokens to encourage players to participate. The story begins here. Since there is no upper limit on SLP, tokens are inflated wildly, prices plummet, and the ecosystem collapses. As the tokens depreciate, players will leave. Axie went from play-for-money darling to cautionary tale almost overnight.

But what if there was a way to solve player liquidity problems that didn’t require unsustainable token economics?

This is exactly what ARC/AI Arena has been silently doing for the past three years.Things to do silently. Now, it's starting to bear fruit.

1. Player mobility is the lifeblood

Player mobility is the lifeblood of multiplayer games and the key to long-term success.

Many Web3 and indie games suffer from the "cold start" problem - too few players to match quickly or form a thriving community. They don't have the marketing budget or natural IP awareness that the big game companies have. This can lead to long wait times, inability to pair, and high churn rates.

These games usually die a slow and painful death.

Therefore, game developers must prioritize player mobility from the beginning. Games require activities of one sort or another to maintain fun - chess requires two players, while large-scale battles require thousands. The skill matching mechanism further raises the bar, requiring more players to keep the game fair and attractive.

For Web3 games, the risk is even greater. According to Delphi Digital's annual gaming report, user acquisition costs for Web3 games are 77% higher than for traditional mobile games, making player retention critical.

A strong player base ensures fair matchmaking, a vibrant game economy (i.e. more buying and selling of items), and more active social interactions, making the game more interesting.

2. ARC - AI gaming pioneer

ARC developed by ArenaX Labs is leading the future of AI online gaming experience. In short, they use AI to solve the player mobility problem that plagues new games.

The problem with most AI bots in games today is that they are terrible. Once you spend a few hours getting the hang of it, these bots become incredibly easy to defeat. They're designed to help new players, but don't provide much challenge or stickiness for experienced players.

Imagine an AI player whose skills rival those of top human players. Imagine being able to play against them anytime, anywhere without having to wait for a match. Imagine training your AI players to imitate your play style, owning it, and earning rewards for their performance.

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

Game companies use human-like AI bots to make their games popular, increase player mobility, improve user experience, and increase retention rates - new game latecomers to survive in a fiercely competitive market key factors.

Players gain a new way to participate in the game, building a stronger sense of belonging in the process of training and competing against AI.

Let’s see how they do it.

3. Products and Architecture

The parent company ArenaX Labs is developing a series of products to solve player mobility problems.

Existing products: 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 game that is reminiscent of Nintendo’s Super Smash Bros is a fighting game where various weird cartoon characters fight in the arena.

But in AI Arena, each character is controlled by AI - you play not a fighter, but their coach. Your task is to use your strategy and expertise to train your AI warriors.

Training your warriors is like training a student to prepare for battle. In training mode, you turn on data collection and create combat scenarios to fine-tune their movements. For example, if your warrior is close to an opponent, you can teach them to block with your shield and then combo. How to fight from a distance? Train them to launch ranged attacks.

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

Everything is difficult at the beginning - training an effective model takes time and experimentation. My first warrior fell off the platform several times, not from being hit by an opponent. But after a few iterations, I managed to create a model that performed well. Seeing your training pay off is deeply satisfying.

AI Arena introduces additional depth with NFT warriors. Each NFT character has unique appearance characteristics and combat attributes, which will affect the gameplay. This adds another layer of strategy.

Currently, AI Arena runs on the Arbitrum mainnet and is only accessible to those with the AI ​​Arena NFT, improving gameplay while maintaining exclusivity for the community. Players can join a guild, gather champion NFTs and NRNs for on-chain battle rankings, and receive rewards. This is done to attract loyal players and drive competition.

Ultimately, AI Arena is a showcase for ARC’s AI training technology. While 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 specifically for games.

The ArenaX team started from scratch, even developing their own gaming infrastructure, because existing solutions like Unity and Unreal couldn't meet their vision.

For more than three years, they carefully designed a powerful technologytechnology stack capable of handling data aggregation, model training, and model checking for imitation and reinforcement learning. This infrastructure is the backbone of AI Arena, but its potential is much greater.

As the team continued to improve their technology, third-party studios began to approach ARC, hoping to obtain authorization or white labeling for the platform. Recognizing this need, they formalized ARC's infrastructure into a B2B product.

Today, ARC works directly with game companies to provide AI gaming experiences. Its value proposition is:

Permanent Player Liquidity as a Service

AI gameplay as a simple integration

Permanent Player Liquidity as a Service

< p>ARC focuses on human behavior cloning—training specialized AI models to imitate human behavior. This is different from the primary use of AI in games today, which uses generative models to create game assets and LLM to drive dialogue.

Using the ARC SDK, developers can create human-like AI agents and scale them according to game needs. The SDK simplifies the heavy lifting. Gaming companies can introduce AI without dealing with complex machine learning.

After integration, deploying an AI model only requires one line of code, and ARC is responsible for infrastructure, data processing, training and back-end deployment.

ARC takes a collaborative approach with game companies to help them:

Capture raw gameplay data and convert it into meaningful datasets for AI training.

Identify key gameplay variables and decision points related to game mechanics.

Map AI model outputs to in-game activities to ensure smooth functionality - for example, linking the AI's "right click" output to specific game controls.

How does AI work?

ARC uses four types of models for game interaction:

Feedforward neural network: suitable for continuous environments with numerical features such as speed or position.

Tabular agents: Particularly ideal for games with limited discrete scenarios.

Hierarchical and convolutional neural networks are under development.

There are two interaction spaces relevant to ARC's AI model:

The state space defines what the agent knows about the game at any given moment. For feedforward networks, this is a combination of input features (such as the player's speed or position). For tabular agents, these are the discrete scenarios the agent may encounter during the game.

The action space describes what an agent can do in a game, from discrete inputs (like pressing a button) to continuous control (like moving a joystick). This maps to game input.

The state space provides input to ARC's AI model, which processes the input and generates output. These outputs are then transformed into game actions through the action space.

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

According to the team, demand for their player liquidity services is particularly high among Web3 companies. These companies pay for better player liquidity, and ARC will use a large portion of this revenue for NRN token buybacks.

Bringing AI gameplay to players: Trainer Platform

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

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

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

The concept of Parallel Play also comes to life for companies that fully integrate agency functionality. AI agents are available 24/7 and can participate in multiple tournaments or game instances simultaneously. This solves player liquidity issues 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 we talk about AI Arena and ARC trainers The platform feels like a single player mode (where you can train your own AI model), then ARC RL is similar to a multiplayer mode.

Imagine this: an entire gaming DAO pools its gameplay data to train a shared AI model that everyone jointly owns and benefits from. These “master agents” represent the collective intelligence of all players, transforming esports by introducing competition driven by collective efforts and strategic collaboration.

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

Reinforcement learning works by rewarding agents for optimal behavior. It works particularly well in games where the reward function is clear and objective, such as damage dealt, gold earned, or victory.

There is precedent for this:

DeepMind’s AlphaGo beat professional human players at the game of Go, trained on millions of self-generated games, with every iteration Perfect your strategy.

I didn’t realize this before, but OpenAI was well known in gaming circles long before chatGPT was created.

OpenAI Five uses augmentation in Dota 2Learn to crush top human players and beat the world champion in 2019. It masters advanced strategies such as teamwork through accelerated simulations and massive computing resources.

OpenAI Five runs millions of games every day, equivalent to 250 years of simulated games every day, and is powerfully supported by 256 GPUs and 128,000 CPUs. By skipping graphics rendering, it greatly speeds up learning.

Initially, the AI ​​exhibited erratic behavior, such as wandering aimlessly, but this quickly improved. It masters basic strategies, such as crawling on trails and stealing resources, and eventually develops into more complex maneuvers, such as ambushes.

The key idea of ​​reinforcement learning is that AI agents learn how to succeed through experience, rather than being directly told what to do.

ARC RL differentiates itself by using offline reinforcement learning. AI agents learn not from their own trial and error, but from the experiences of others. It's like being a student watching videos of others riding bikes, observing their successes and failures, and using that knowledge to avoid falling and improve faster.

This approach provides an added benefit: collaborative training and shared ownership of the model. Not only does this make powerful AI agents more common, it also makes the motivations of players, guilds, and developers more aligned.

In the creation of "super-intelligent" game agents, there are two key roles:

Sponsors: similar to the leaders of guilds, they pledge large amounts of NRN tokens to start and manage RL agent. The sponsor can be any entity, but is likely to be a gaming guild, a DAO, the web3 community, or even a popular on-chain personalization agent like Luna.

Player: An individual who stakes a small amount of NRN tokens to contribute their gameplay data to train the agent.

Sponsors coordinate and coach their teams of players to ensure high-quality training data to give their AI agents a competitive advantage in agent competitions.

Rewards are distributed based on the performance of super agents in the competition. 70% of the rewards go to the players, 10% goes to the sponsors, and the remaining 20% ​​goes to the NRN treasury. This structure aligns incentives for all participants.

Data Contribution

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

ARC makes providing gameplay data easy and rewarding. Players do not need professional knowledge, they just need to 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, impactful data.

The interesting thing is that even if you are a terrible player (like me), your data is useful. Bad gameplay can helpScience teaches you what not to do, while skilled gameplay teaches optimal strategies. Redundant data is filtered out to maintain quality.

In short, ARC RL is designed as a low-friction, mass-market product centered around agents who collectively possess superhuman abilities.

4. Market size

ARC's technology platform is multi-functional and supports multiple types of games, such as shooting games, fighting games, social casinos, racing, card trading games and RPGs. It's tailor-made for games that need to keep players engaged.

ARC's products are mainly targeted at two markets:

ARC's main focus is independent developers and companies, rather than established manufacturers. Due to limited brand influence and distribution resources, these small companies often have difficulty attracting players in the early stages.

ARC's AI agents solve this problem by creating a dynamic game environment from the start, ensuring dynamic gameplay even in the initial stages of the game.

This may surprise many people, but the independent game sector is indeed a major force in the game market:

99% of games on Steam are Indie game.

In 2024, independent games will generate 48% of total revenue on Steam.

Another target market is Web3 games. Most Web3 games are developed by emerging companies, which also face unique challenges such as wallet login, encryption doubts and high user acquisition costs. These games often have player mobility issues, and AI agents can fill in the gaps and keep the game attractive.

While Web3 games have struggled recently due to a lack of engaging experiences, they are showing signs of recovery.

For example, Off the Grid, one of the earliest AAA Web3 games, recently achieved early mainstream success, with 100 million transactions across 9 million wallets in its first month. This paves the way for widespread success in the industry, creating opportunities for ARC to support this renaissance.

5. ARC Team

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

CEO and CTO Brandon Da Silva once led machine learning research at a Canadian investment company, focusing on reinforcement learning, Bayesian deep learning and model adaptability. He pioneered the development of 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 strategy portfolio at the same company and presides over its innovative investment projects, focusing on emerging areas such as AI, machine learning and Web3 technology.

ArenaX Labs received $5 million in seed funding in 2021, led by ParadigmLeading the investment, with participation from Framework Ventures. The company raised $6 million in funding in January 2024, led by SevenX Ventures, FunPlus/Xterio, and Moore Strategic Ventures.

6. NRN Token Economics - A Healthy Reform

ARC/AI Arena has a token - NRN. Let us first take stock of the current situation.

Examining the supply side and the demand side will give us a clearer picture of where the trends are headed.

(1) Supply side

The total supply of NRN is 1 billion, of which approximately 409 million (40.9%) are in circulation.

At the time of writing, the token is priced at $0.72, which implies a market cap of $29 million and a fully diluted valuation of $71 million.

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

Community airdrops (8% of the total)

Foundation vault (accounting for 10.9%, of which 2.9% has been unlocked, linearly unlocked in 36 months)

Community Ecosystem Rewards (30%)

The majority of the circulating supply (30% of 40.9%) consists of community ecosystem rewards, with projects managing these tokens and Strategically allocated to staking rewards, game rewards, ecosystem growth initiatives, and community-driven initiatives.

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 in December 2024, 12 Monthly linear unlock. This would only increase monthly inflation by 0.09% and is unlikely to cause major concern.

The investor and contributor allocation (50% of the total supply) will not begin to unlock until June 2025, and even then, it will be unlocked linearly over 24 months.

For now, selling pressure is expected to remain fairly controllable, mainly due to ecosystem rewards. The key is to trust the team’s ability to deploy these funds strategically to fuel the growth of the protocol.

(2) Demand side

NRN v1 - Player Economy

Initially, NRN was designed to be compatible with the AI ​​Arena game economy associated strategic resources.

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

Rewards are distributed using the ELO system, ensuring balanced payouts based on skill. Other revenue sources include game item purchases, cosmetic upgrades and competition entry fees.

Initial tokensThe model relies entirely on the success of the game and the continued willingness of new players to purchase NRN and NFT to participate in the game.

Let’s talk about why we are so excited...

NRN v2 - Player & Platform Economy

NRN improved v2 Tokenomics introduces powerful new demand drivers by extending the utility of tokens from AI Arena to the broader ARC platform. This evolution transforms NRN from a game-specific token into a platform token. In my opinion, this is a very positive change.

Three new demand drivers for NRN include:

Revenue from ARC integration. Game companies integrating ARC will generate revenue for the coffers through integration fees and ongoing royalties tied to game performance. Vault funds can drive NRN buybacks, grow the ecosystem, and incentivize players on the trainer platform.

Trainer market fees. NRN derives value from trainer farm fees, and players can trade AI models and gameplay data on the trainer market.

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

Particularly exciting are the gains for gaming companies. This marks a shift from a pure B2C model to a hybrid B2C and B2B model, creating continued external capital inflows into the NRN economy. With ARC having a wider target market, this revenue stream will exceed what AI Arena itself can generate.

The trainer market fee, while promising, is dependent on the ecosystem reaching critical mass - enough games, trainers, and players to maintain active trading activity. This is a long-term undertaking.

In the short term, ARC RL staking may be the most immediate and reflexive demand driver. A well-funded initial reward pool and excitement about a new product launch may spark early adoption, boosting token prices and attracting participants. This creates a feedback loop of rising demand and economic growth. However, on the other hand, if ARC RL has difficulty maintaining user stickiness, demand may disappear quickly.

The potential for network effects is huge: more games → more players → more games added → more players. This virtuous cycle can position NRN as a core token in the Crypto AI gaming ecosystem.

7. The mother of game AI models

What is the ending? The advantage of ARC is its ability to promote various game types. Over time, allowing them to amass a unique database of specific gameplay. As ARC integrates with more games, it can continuously feed this data back into its own ecosystem, creating a virtuous cycle of growth and improvement.

Once this cross-sectional game data set reaches criticalquality, it will be a very valuable resource. Imagine using it to train a universal AI model for game development - opening up new possibilities for designing, testing and optimizing games at scale.

It’s still early days, but in the era of artificial intelligence where data is the new oil, the potential is limitless.

8. Our thoughts

(1) NRN evolves into a platform game - token repricing

With the release of ARC and ARC RL, the project is no longer just a single product ’s gaming company, it now positions itself as a platform and AI game. This shift should lead to a re-rating of the NRN token, which was previously limited by the success of AI Arena. The introduction of new token sources through ARC RL, coupled with revenue share agreements with gaming companies and external demand for trainer transaction fees, creates a broader and more diverse base for NRN’s utility and value.

(2) Success is closely tied to gaming partners

ARC’s business model ties its success to the companies it partners with, as the revenue stream is based on token distribution (in Web3 games) and payment of game royalties. The games that tie into it are worth a look.

If the ARC game is a huge success, the resulting value will flow back to NRN holders. Conversely, if a cooperative game gets bogged down, the value flow will be limited.

(3) Looking forward to more integration with Web3 games

The ARC platform is very suitable for Web3 games. In Web3 games, competitive gameplay with incentive mechanisms and the existing token economy Perfect combination.

By integrating ARC, Web3 games can immediately enter the "AI agent" narrative. ARC RL brings communities together and inspires them toward common goals. This also opens up new opportunities for innovative mechanics, such as making events such as “Game to Airdrop” more attractive to players. By combining AI and token incentives, ARC adds a depth and excitement that traditional games cannot replicate.

(4) AI gameplay has a learning curve

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

However, ARC RL’s player experience is less frictional because the AI ​​training is handled on the backend when players play the game and submit data. Another open question is how players will feel when they know their opponent is an AI. Does this affect them? Will it enhance or diminish the gaming experience? Only time will tell.

9. Bright future

AI will open up a new breakthrough experience in the game world.

Teams like Parallel Colony and Virtuals are driving the development of autonomous AI agents, while ARC is doing so by focusing on humansBehavioral clones to carve out their own niche – providing an innovative way to solve player liquidity challenges without relying on unsustainable token economics.

The transition from games to full-fledged platforms 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 potential for improved token economics and powerful network effects, ARC’s bright future seems to be just beginning.