Article reprinted from: Plain Blockchain
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 experience that era, let me tell you—it was absolutely wild.
The game Axie Infinity made people realize that cryptocurrency and gaming could be combined. Essentially, it's a simple Pokémon-style strategy game where players build a team of 3 Axies (fierce warriors), each with unique abilities. You can lead your team to battle other teams and earn SLP token rewards by participating in the game and winning.
But what excites non-gamers is the potential to earn money through gaming. The rapid rise of Axie was due to two major mechanisms:
The first is Breeding Axies. Acquire two Axies, breed them using SLP tokens, and voilà—a new Axie is born, combining the unique abilities of the original two Axies. Thus, rare and powerful Axies (known as OP Axies by players) become hot commodities, creating a bustling breeding market.
The second mechanism is the scholarship program. Business players from around the world began lending Axies to 'scholars'. These players often come from developing countries like the Philippines or Argentina, where they cannot afford the upfront costs of over $1,000 to buy 3 Axie NFTs. Scholars earn tokens by playing games daily and share profits with the scholarship guilds, which typically take a cut of 30-50%.
At its peak, especially during the 2019 pandemic, Axie had a significant impact on local economies in developing countries. In the Philippines (where about 40% of Axie Infinity users are located), many players earned far above the minimum wage. Guilds profited handsomely.
This addresses a key issue for game developers: player liquidity. By incentivizing players to actively play for hours each day, Axie ensures that every player has an opponent waiting, making the experience more engaging.
But this comes at a cost.
To address player liquidity issues, Axie distributed a large number of tokens to incentivize player participation. The story begins here. Since SLP had no cap, the token inflated wildly, prices plummeted, and the ecosystem collapsed. When the token depreciated, players left. Axie went from 'play-to-earn' darling to a cautionary tale almost overnight.
But what if there were a way to solve the player liquidity issue without relying on unsustainable token economics?
This is precisely what ARC / AI Arena has been quietly working on for the past three years. Now, it's starting to bear fruit.
1. Player liquidity is the lifeblood
Player liquidity is the lifeblood of multiplayer games and is key to long-term success.
Many Web3 and indie games face a 'cold start' problem—too few players to quickly match or form thriving communities. They lack the marketing budgets or natural IP recognition that established game companies possess. This leads to long wait times, inability to match, and high churn rates.
These games often slowly and painfully fade away.
Therefore, game developers must prioritize player liquidity from the start. Games need various activities to maintain fun—chess requires two players, while large-scale battles need thousands. Skill-matching mechanisms further raise the bar, requiring more players to maintain fairness and attractiveness in games.
Web3 games face greater risks. According to Delphi Digital's annual gaming report, the user acquisition cost for Web3 games is 77% higher than traditional mobile games, making player retention critical.
A strong player base can ensure fair matchmaking, a vibrant game economy (i.e., more trading of items), and more active social interactions, making the game more enjoyable.
2. ARC—AI Gaming Pioneer
Developed by ArenaX Labs, ARC is leading the future of AI online gaming experiences. In short, they use AI to solve the player liquidity issues that plague new games.
The problem with most in-game AI bots today is that they are too weak. Once you've spent a few hours mastering the tricks, these bots become very easy to defeat. They are designed to help new players but don't present much challenge or stickiness for experienced players.
Imagine that the skills of AI players rival those of top human players. Imagine being able to compete against them anytime, anywhere, without waiting for matchmaking. Imagine training your AI player to mimic your gameplay style, possessing it, and earning rewards based on its performance.
This is a win-win for both players and game companies.
Game companies use human-like AI bots to make games more appealing, enhancing player liquidity, improving user experience, and increasing retention - a key factor for new game entrants to survive in a competitive market.
Players gain a new way to engage with the game, building a stronger sense of belonging through training AI and competing against it.
Let's see how they're doing.
3. Products and Architecture
Parent company ArenaX Labs is developing a range of products to address player liquidity issues.
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 reminiscent of Nintendo's Super Smash Bros, featuring a variety of quirky cartoon characters battling in an arena.
But in AI Arena, each character is controlled by AI—you are not the fighter, but their coach. Your task is to use your strategies and expertise to train your AI warrior.
Training your warrior is like preparing a student for battle. In training mode, you enable data collection and create battle scenarios to fine-tune their actions. For example, if your warrior is close to an opponent, you can teach it to block with your shield and then counterattack. 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. After practice, you can refine hyperparameters for more technical advantages or simply use beginner-friendly default settings. Once training is complete, your AI warrior is ready to enter battles.
Starting anything is challenging—training an effective model takes time and experimentation. My first warrior fell off the platform several times, not because it was hit by an opponent. But after several iterations, I successfully created a well-performing model. Seeing your training pay off is extremely satisfying.
AI Arena introduces additional depth through NFT warriors. Each NFT character has unique visual traits and combat attributes, which affect gameplay. This adds another layer of strategy.
Currently, AI Arena runs on the Arbitrum mainnet, and only those with AI Arena NFTs can access it, maintaining exclusivity for the community while refining gameplay. Players can join guilds, gather champion NFTs and NRN to compete in on-chain battle rankings and earn rewards. This is designed to attract loyal players and drive competition.
Ultimately, AI Arena serves as the showcase for ARC's AI training technology. While this is their entry point into the ecosystem, the true vision extends far beyond this game itself.
(2) ARC: Infrastructure
ARC is an AI infrastructure solution designed specifically for gaming.
The ArenaX team started from scratch, even developing their own game infrastructure because existing solutions like Unity and Unreal couldn't meet their vision.
Over the past three years, they have meticulously designed a robust tech stack capable of handling data aggregation, model training, and model verification for imitation and reinforcement learning. This infrastructure is the backbone of AI Arena, but its potential is far greater.
As the team continually refines their technology, third-party studios are beginning to approach ARC for licensing or white-labeling opportunities. Recognizing this demand, they formalized ARC's infrastructure into a B2B product.
Today, ARC collaborates directly with game companies to provide AI gaming experiences. Its value proposition is:
Permanent player liquidity as a service
Integrating AI gameplay as a simple integration
Permanent player liquidity as a service
ARC focuses on human behavior cloning—training specific AI models to mimic human behavior. This differs from the primary use of AI in games today, which uses generative models to create game assets and LLMs to drive conversations.
Using the ARC SDK, developers can create human-like AI agents and scale them according to game needs. The SDK simplifies the heavy lifting. Game companies can introduce AI without dealing with complex machine learning.
Once integrated, deploying AI models requires just one line of code, with ARC handling infrastructure, data processing, training, and backend deployment.
ARC collaborates with game companies to help them:
Capturing raw gameplay data and converting it into meaningful datasets for AI training.
Identifying key gameplay variables and decision points related to game mechanics.
Mapping AI model outputs to in-game activities ensures 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 gaming interaction:
Feed-forward neural networks: Suitable for continuous environments with numerical features like speed or position.
Table agents: Particularly ideal for games with limited discrete scenarios.
Hierarchical and convolutional neural networks are in development.
There are two interactive spaces associated with ARC's AI models:
The state space defines what the agent knows about the game at any given moment. For feed-forward networks, this is a combination of input features (like player speed or position). For table agents, it consists of discrete scenarios the agent might encounter in the game.
The action space describes what agents can do in the game, from discrete inputs (like pressing a button) to continuous controls (like joystick movements). This maps to game inputs.
The state space provides inputs for ARC's AI models, which process the inputs and generate outputs. These outputs are then 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, there is particularly high demand from Web3 companies for their player liquidity services. These companies pay for better player liquidity, and ARC will allocate a significant portion of this revenue for NRN token buybacks.
Bringing AI gameplay to players: Trainer Platform
The ARC SDK also allows web3 companies to access their game's trainer platform, enabling players to train and submit agents.
Like AI Arena, players can set up simulations, gather gameplay data, and train blank AI models. These models will evolve over time, integrating new gameplay data while retaining previous knowledge, eliminating the need to start from scratch with each update.
This opens exciting possibilities: players can sell their custom-trained AI agents on the market, creating a new layer of in-game economy. In AI Arena, skilled trainers can form guilds and offer training skills to other companies.
For companies that fully integrate agent capabilities, the concept of Parallel Play becomes lively. AI agents are available 24/7, capable of participating in multiple matches 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-on-one to many-to-one
If AI Arena and the ARC trainer platform feel like single-player mode (where you can train your AI models), then ARC RL is akin to multiplayer mode.
Imagine this: an entire game DAO gathers its gameplay data to train a shared AI model, which everyone collectively owns and benefits from. These 'master agents' represent the collective wisdom of all players, transforming esports through competition driven by collective effort and strategic cooperation.
ARC RL uses reinforcement learning (i.e., 'RL') and crowdsourced human gameplay data to train these 'superintelligent' agents.
The key idea of reinforcement learning is that agents are rewarded for optimal behavior. It's particularly effective in games because the reward mechanism is clear and objective, such as damage dealt, gold earned, or victories achieved.
This has precedent:
DeepMind's AlphaGo defeated professional human players in Go competitions through millions of self-generated matches, refining its strategies with each iteration.
I didn't realize this before, but even before chatGPT was created, OpenAI was already well-known in the gaming circle.
OpenAI Five used reinforcement learning to crush top human players in Dota 2, defeating the world champions in 2019. It mastered advanced strategies like teamwork through accelerated simulations and massive computational resources.
OpenAI Five runs millions of games daily, equivalent to 250 years of simulated games each day, powered by 256 GPUs and 128,000 CPUs. By skipping graphic rendering, it significantly accelerated the learning speed.
Initially, the AI exhibited unstable behavior, like wandering aimlessly, but quickly improved. It mastered basic strategies such as crawling along paths and resource theft, eventually evolving into complex maneuvers like ambushes.
The key idea of reinforcement learning is that AI agents learn to succeed through experience rather than being directly told what to do.
ARC RL distinguishes 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 a student watching videos of others riding a bike, 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 models. This not only makes powerful AI agents more accessible but also aligns the incentives of players, guilds, and developers more closely.
In creating 'superintelligent' game agents, there are two key roles:
Sponsors: Leaders similar to guilds who stake a large amount of NRN tokens to initiate and manage RL agents. Sponsors can be any entity but are likely game guilds, DAOs, web3 communities, or even popular on-chain personalized agents like Luna.
Players: Individuals who stake a small amount of NRN tokens to contribute their gameplay data for training agents.
Sponsors coordinate and guide their player teams, ensuring high-quality training data that gives their AI agents a competitive edge in agent matches.
Rewards are distributed based on the performance of super agents in matches. 70% of the rewards go to players, 10% to sponsors, and the remaining 20% to the NRN treasury. This structure provides all participants with a consistent incentive mechanism.
Data Contribution
How do you encourage players to willingly contribute their gameplay data? It's not easy.
ARC makes providing gameplay data simple and rewarding. Players do not need expertise; they just play the game. After a session, they are prompted to submit data to train a specific agent. A dashboard tracks their contributions and the agents they support.
ARC's attribution algorithm ensures quality by evaluating contributions and rewarding high-quality, impactful data.
Interestingly, even if you're a bad player (like me), your data is still useful. Poor gameplay can help agents learn what not to do, while skilled gameplay can teach the best strategies. Redundant data is filtered out to maintain quality.
In short, ARC RL is designed as a low-friction mass-market product centered around jointly owning agents that surpass human capabilities.
4. Market Size
ARC's technology platform is multifunctional, supporting various types of games, such as shooters, fighting games, social casinos, racing, trading card games, and RPGs. It is tailored for games that need to maintain player stickiness.
ARC's products primarily target two markets:
ARC primarily focuses on indie developers and companies rather than established giants. These smaller companies often struggle to attract players early on due to limited brand influence and distribution resources.
ARC's AI agents tackle this issue by creating a vibrant gaming environment from the start, ensuring dynamic gameplay even in the initial phases of the game.
This may surprise many, but the indie game sector is indeed a major force in the gaming market:
99% of games on Steam are indie games.
In 2024, indie games generated 48% of total revenue on Steam.
Another target market is Web3 games. Most Web3 games are developed by emerging companies facing unique challenges such as wallet logins, crypto skepticism, and high user acquisition costs. These games often suffer from player liquidity issues, and AI agents can fill the gap to maintain game appeal.
While Web3 games have recently struggled due to a lack of engaging experiences, signs of recovery are emerging.
For example, one of the earliest AAA Web3 games, Off the Grid, recently achieved early mainstream success, with 9 million wallets making 100 million transactions in its first month. This paved the way for widespread success in the industry and created opportunities for ARC to support this revival.
5. The ARC Team
The founding team behind ArenaX Labs has extensive expertise in machine learning and investment management.
CEO and CTO Brandon Da Silva previously led machine learning research at a Canadian investment firm, focusing on reinforcement learning, Bayesian deep learning, and model adaptability. He pioneered a $1 billion quantitative trading strategy centered on risk parity and multi-asset portfolio management.
COO Wei Xie manages a $7 billion liquidity strategy portfolio at the same company and oversees its innovation investment projects, focusing on emerging fields like AI, machine learning, and Web3 technology.
ArenaX Labs raised $5 million in seed funding in 2021, led by Paradigm and Framework Ventures. The company secured $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's first take stock of the current situation.
Examining both the supply side and demand side will give us clearer insights into the trends.
(1) Supply Side
The total supply of NRN is 1 billion, with approximately 409 million (40.9%) in circulation.
At the time of writing, the token price is $0.72, meaning a market capitalization of $29 million, with a fully diluted valuation of $71 million.
NRN was launched on June 24, 2024, with 40.9% of the circulating supply coming from:
Community airdrops (8% of total)
Foundation treasury (10.9%, of which 2.9% is unlocked, with a 36-month linear unlock)
Community ecosystem rewards (30% of total)
Most of the circulating supply (30% of 40.9%) consists of community ecosystem rewards, with the project managing these tokens and strategically allocating them to staking rewards, game rewards, ecosystem growth programs, 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 sale (1.1%), starting in December 2024, with a 12-month linear unlock. This will only increase the monthly inflation rate by 0.09%, unlikely to raise significant concerns.
The allocation for investors and contributors (50% of total supply) won't begin unlocking until June 2025, and even then, it will be a linear unlock over 24 months.
Currently, selling pressure is expected to remain relatively manageable, primarily stemming from ecosystem rewards. The key is to trust the team to strategically deploy these funds to drive protocol growth.
(2) Demand Side
NRN v1—Player Economy
Initially, NRN was designed as a strategic resource associated with the AI Arena game economy.
Players stake NRN on AI players, earning rewards if they win and losing part of their stake if they lose. 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, ensuring skill-based balanced payouts. Other sources of revenue include game item purchases, cosmetic upgrades, and entry fees for competitions.
The initial token model relied entirely on the success of the game and the continuous willingness of new players to purchase NRN and NFTs to participate in the game.
Now let's talk about why we are so excited...
NRN v2—Player & Platform Economy
The improved v2 token economics of NRN introduces powerful new demand drivers by expanding the utility of the token from AI Arena to the broader ARC platform. This evolution transforms NRN from a specific game token into a platform token. In my view, this is a very positive shift.
Three new demand drivers for 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 drive NRN buybacks, grow the ecosystem, and incentivize players on the trainer platform.
Trainer market fees. NRN derives value from fees charged in the trainer market, allowing players to trade AI models and gameplay data.
Participation 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 will correspondingly increase.
What is especially exciting is the revenue for game companies. This marks a shift from a purely B2C model to a mixed B2C and B2B model, creating a sustained inflow of external capital into the NRN economy. As ARC targets a broader market, this revenue stream will exceed what AI Arena itself can generate.
While the fees in the trainer market are promising, they depend on whether the ecosystem can reach a critical scale—enough games, trainers, and players to sustain active trading activity. This is a long-term endeavor.
In the short term, ARC RL staking may be the most direct and reflexive demand driver. A well-funded initial reward pool and the excitement of new product launches could trigger early adoption, driving up token prices and attracting participants. This creates a feedback loop of rising demand and economic growth. However, conversely, if ARC RL struggles to maintain user stickiness, demand could quickly evaporate.
The potential for network effects is huge: more games → more players → more games joining → more players. This virtuous cycle positions NRN as the core token in the Crypto AI gaming ecosystem.
7. The Mother of Game AI Models
What is the outcome? ARC's advantage lies in its ability to promote a variety of game types. Over time, they can collect a unique database of specific gameplay. As ARC integrates with more games, it can continuously feed this data back into its ecosystem, creating a virtuous cycle of growth and refinement.
Once this cross-sectional gameplay dataset reaches critical quality, it will become a highly valuable resource. Imagine using it to train general AI models for game development—opening new possibilities for large-scale design, testing, and optimization.
It's still early days, but in the age of data being the new oil, the potential in this area is limitless.
8. Our Ideas
NRN evolves into a platform game—token repricing
With the launch of ARC and ARC RL, the project is no longer just a single product game company; it now positions itself as a platform and AI gaming service. This shift should lead to a repricing of the NRN token, which was previously tied to the success of AI Arena. The introduction of new token sources through ARC RL, coupled with revenue-sharing agreements with game companies and external demand for trainer transaction fees, creates a broader and more diverse foundation for NRN's utility and value.
Success is closely tied to 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 royalty payments. The games closely tied to this are worth a look.
If ARC games achieve great success, the resulting value will flow back to NRN holders. Conversely, if partnered games struggle, the value flow will be restricted.
Expect more integrations with Web3 games
The ARC platform is well-suited for Web3 games, where competitive gameplay with incentive mechanisms perfectly aligns with existing token economics.
By integrating ARC, Web3 games can immediately enter the 'AI agent' narrative. ARC RL brings the community together, motivating them towards common goals. This also opens new opportunities for innovative mechanisms, such as making activities like 'game-to-airdrop' more engaging for players. By combining AI and token incentives, ARC adds depth and excitement that traditional games cannot replicate.
AI gameplay has a learning curve
AI gameplay has a steep learning curve, which may create friction for new players. It took me an hour to figure out how to properly train my player in AI Arena.
However, the player experience friction in ARC RL is less because AI training is handled in the backend while players play the game and submit data. Another pending question is how players feel when they know their opponent is AI. Does this affect them? Does it enhance or detract from the gaming experience? Only time will tell.
9. Bright Future
AI will unlock groundbreaking experiences in the gaming world.
Teams like Parallel Colony and Virtuals are pushing the development of autonomous AI agents, while ARC carves out its niche by focusing on human behavior cloning—offering an innovative approach to tackle player liquidity challenges without relying on unsustainable token economics.
The transition from game to mature platform is a significant leap for ARC. It not only opens up greater opportunities through collaboration with game companies but also redefines the integration of AI with gaming.
With its improved token economics and strong potential for network effects, the bright path for ARC seems to be just beginning.
