Author: Teng Yan, Chain of Thought; Translation: Jinse Finance Xiaozou
In 2021, I was an Axie Infinity player running a small scholarship guild. If you haven’t experienced that era, let me tell you — it was absolutely wild.
The game Axie Infinity made people aware that cryptocurrency and gaming could come together. Essentially, it's a simple Pokémon-style strategy game where players build a team of 3 Axies (extremely fierce warriors), each with unique abilities. You can lead your team against others and earn SLP token rewards by participating in the game and winning.
But what really excites non-gamers is the potential to make money through gaming. The rapid rise of Axie was due to two mechanisms:
The first is Breeding Axies. Obtain two Axies, breed them using SLP tokens, and voilà — a new Axie arises, combining the unique abilities of the two original Axies. Thus, rare and powerful Axies (known as OP Axies by gamers) became hot commodities, leading to a bustling breeding market.
The second mechanism is the scholarship program. Business players from around the world start lending Axies to 'scholars.' These players often come from developing countries like the Philippines or Argentina, where they cannot afford the upfront cost of over $1,000 to buy 3 Axie NFTs. Scholars earn tokens by playing games daily and share profits with scholarship guilds, which usually take a 30-50% cut.
At its peak, especially during the pandemic in 2019, Axie had a significant impact on local economies in developing countries. In the Philippines (where about 40% of Axie Infinity users are), many players earned far above the minimum wage. Guilds profited handsomely.
This addresses a critical issue for game developers: player liquidity. By incentivizing players to actively engage in the game for several hours every day, Axie ensures that every player has an opponent waiting there, making the experience more engaging.
But this comes at a cost.
To tackle player liquidity issues, Axie distributed large amounts of tokens to incentivize player participation. The story begins here. With no upper limit on SLP, the tokens inflated wildly, causing prices to plummet and the ecosystem to collapse. As token values depreciated, players left. Axie rapidly transitioned from a 'play-to-earn' darling to a cautionary tale almost overnight.
But what if there was a way to solve player liquidity issues without relying on unsustainable token economics?
This is exactly what ARC/AI Arena has been quietly working on for the past three years. Now, it is starting to bear fruit.
1. Player liquidity is vital
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 larger game studios 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 require varied activities to maintain fun — chess needs two players, while massive battles need thousands. Skill matching mechanisms raise the bar further, requiring more players to keep games fair and appealing.
For Web3 games, the risks are greater. According to Delphi Digital's annual gaming report, the cost of customer acquisition for Web3 games is 77% higher than for traditional mobile games, making player retention critical.
A strong player base can ensure fair matchmaking, a vibrant game economy (i.e., more item trading), and more active social interactions, making games more enjoyable.
2. ARC — AI Game Pioneer
Developed by ArenaX Labs, ARC is leading the future of AI online gaming experiences. In short, they use AI to address the player liquidity issues that plague new games.
The problem with most AI bots in games today is that they are too poor. Once you spend a few hours mastering the tricks, these bots become very easy to defeat. They are designed to help new players but don't provide much challenge or stickiness for experienced players.
Imagine AI players' skills matching those of top human players. Imagine being able to challenge them anytime and anywhere without waiting for matchmaking. Imagine training your AI player to mimic your gameplay style, mastering it, and earning rewards through its performance.
This is a win-win for both players and game companies.
Gaming companies use human-like AI bots to make games popular, enhance player liquidity, improve user experience, and increase retention — this is a key factor for new entrants in the fiercely competitive market.
Players gain a new way to engage with games, fostering a stronger sense of belonging through the process of training AI and competing against them.
Let's take a look at how they do it.
3. Products and Architecture
Parent company ArenaX Labs is developing a series 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: The 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 warrior but their coach. Your task is to use your strategies and expertise to train your AI warriors.
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 the opponent, you can teach it to block with your shield and then 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 moves are recorded for training. With practice, you can refine hyperparameters for more technical advantages or simply use beginner-friendly defaults. Once training is complete, your AI warrior can enter combat.
Everything is difficult at the beginning — training an effective model takes time and experimentation. My first warrior fell off the platform several times, not because of being hit by an opponent. But after several iterations, I successfully created a model that performed well. It's deeply satisfying to see your training pay off.
AI Arena introduces additional depth through NFT warriors. Each NFT character has unique appearance traits and combat attributes, which affect gameplay. This adds another layer of strategy.
Currently, AI Arena operates on the Arbitrum mainnet, and only those with AI Arena NFTs have access, maintaining community exclusivity while refining gameplay. Players can join guilds, gather champion NFTs and NRN for on-chain battle rankings, and earn rewards. This is done 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 just this game.
(2) ARC: Infrastructure
ARC is an AI infrastructure solution designed specifically for gaming.
The ArenaX team started from scratch and even developed their own gaming infrastructure because existing solutions like Unity and Unreal couldn't meet their vision.
Over the course of three years, they meticulously designed a robust tech stack capable of handling data aggregation, model training, and model validation for imitation and reinforcement learning. This infrastructure is the backbone of AI Arena, but its potential is much larger.
As the team continues to refine their technology, third-party studios are beginning to approach ARC for licensing or white-labeling the platform. Recognizing this demand, 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
Integrating 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 differs from the primary use of AI in games today, which employs 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 requirements. The SDK simplifies the heavy lifting. Game companies can introduce AI without dealing with complex machine learning.
After integration, deploying AI models requires just one line of code, with ARC handling the infrastructure, data processing, training, and backend deployment.
ARC adopts a collaborative approach to work with game companies, helping them:
Capture raw gameplay data and transform 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, ensuring smooth functionality — for instance, linking the AI's 'right click' output to specific game controls.
How does AI work?
ARC employs four types of models for gaming interactions:
Feedforward Neural Networks: Suitable for continuous environments with numerical features like speed or position.
Table Agents: Especially ideal for games with limited discrete scenarios.
Hierarchical and Convolutional Neural Networks are in development.
There are two interactive spaces related to ARC's AI models:
The state space defines what the agent knows about the game at any given moment. For feedforward networks, it's a combination of input features (like player speed or position). For table agents, it's the discrete scenarios the agent may encounter in the game.
The action space describes what the agent can do in the game, ranging from discrete inputs (like pressing buttons) to continuous control (like joystick movement). This will be mapped to game inputs.
The state space provides input for ARC's AI models, which process the input to generate output. 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 game operations.
According to the team, there is particularly high demand from Web3 companies for their player liquidity services. These companies are paying for better player liquidity, and ARC will funnel a significant portion of this revenue into NRN token buybacks.
Bringing AI gameplay to players: the trainer platform
The ARC SDK also allows web3 companies to access their training 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 evolve over time, integrating new gameplay data while retaining previous knowledge, eliminating 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 layer of in-game economy. In AI Arena, skilled trainers can form guilds to offer training skills to other companies.
For companies fully integrating agent capabilities, the concept of Parallel Play becomes vivid. AI agents are available around the clock and can participate 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-on-one
If AI Arena and the ARC trainer platform feel like single-player modes (where you can train your own AI models), then ARC RL is akin to multiplayer mode.
Imagine: an entire game DAO pooling its gameplay data to train a shared AI model, with everyone co-owning the model and benefiting from it. These 'master agents' represent the collective intelligence of all players, revolutionizing esports through competition driven by collective effort and strategic collaboration.
ARC RL uses reinforcement learning (i.e., 'RL') and crowdsourced human gameplay data to train these 'super-intelligent' agents.
Reinforcement learning works by rewarding agents for optimal behaviors. It is particularly effective in games because the reward functions are clear and objective, such as damage dealt, coins earned, or victories.
This is not without precedent:
DeepMind's AlphaGo defeated professional human players in Go competitions, training through millions of self-generated matches, refining its strategy with each iteration.
I wasn't aware of 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 champion in 2019. It mastered advanced strategies like teamwork via accelerated simulations and immense computational resources.
OpenAI Five runs millions of games daily, equivalent to 250 years of simulations each day, powered by 256 GPUs and 128,000 CPUs. It greatly accelerates learning by skipping graphical rendering.
Initially, the AI displayed unstable behavior, like wandering aimlessly, but quickly improved. It mastered basic strategies like crawling along paths and resource theft, eventually evolving into complex maneuvers like ambushes.
The key concept 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 from others' experiences rather than through trial and error. It's like 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 additional benefit: collaborative training and shared ownership of models. This not only makes powerful AI agents more accessible but also aligns the motivations of players, guilds, and developers.
In the creation of 'super-intelligent' game agents, there are two key roles:
Sponsors: Guild-like leaders who stake large amounts of NRN tokens to start and manage RL agents. Sponsors can be any entity but are likely to be game guilds, DAOs, web3 communities, or even popular on-chain personalized agents like Luna.
Players: Individuals who stake small amounts of NRN tokens to contribute their gameplay data to train agents.
Sponsors coordinate and guide their player teams, ensuring high-quality training data, giving 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 get players to willingly contribute their gameplay data? It's not easy.
ARC makes it simple and rewarding to provide gameplay data. Players don’t need expertise; they just need to 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 the agent 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 co-owning agents that surpass human capabilities.
4. Market Size
ARC's technology platform is multifunctional, supporting various types of games, including shooting games, fighting games, social casinos, racing, trading card games, and RPGs. It is tailored for games requiring player stickiness.
ARC's products primarily target two markets:
ARC primarily focuses on indie developers and companies rather than established giants. Due to limited brand influence and distribution resources, these smaller companies often struggle to attract players early on.
ARC's AI agents address this issue by creating a vibrant gaming environment from the start, ensuring dynamic gameplay even in the early stages of the game.
This may surprise many, but the independent game sector is indeed a major force in the gaming market:
99% of the 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 that also face unique challenges such as wallet logins, crypto skepticism, and high customer acquisition costs. These games often encounter player liquidity issues, and AI agents can fill the gap to keep the games engaging.
While Web3 games have recently struggled due to a lack of engaging experiences, signs of revival are emerging.
For instance, one of the earliest AAA-level 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 broad success in the industry, creating an opportunity 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 innovative investment projects, focusing on emerging fields like AI, machine learning, and Web3 technologies.
ArenaX Labs raised $5 million in seed funding in 2021, led by Paradigm 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's first take stock of the current situation.
Examining 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, of which approximately 409 million (40.9%) is in circulation.
As of this writing, the token price is $0.72, which means a market cap of $29 million, with a fully diluted valuation of $71 million.
NRN is set to launch on June 24, 2024, with 40.9% of the circulating supply coming from:
Community airdrop (8% of total)
Foundation treasury (10.9%, of which 2.9% is unlocked, with a 36-month linear unlock)
Community ecosystem rewards (30%)
Most of the circulating supply (30% of 40.9%) consists of community ecosystem rewards, with the project managing these tokens and strategically distributing them for staking rewards, gaming rewards, ecosystem growth programs, and community-driven initiatives.
The unlock 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 distribution for investors and contributors (50% of total supply) will not begin unlocking until June 2025, and even then, it will be a 24-month linear unlock.
Currently, the sell-off pressure is expected to remain relatively controllable, primarily stemming from ecosystem rewards. The key is to trust that the team can strategically deploy these funds to drive the protocol's 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 dynamic of direct incentives, 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 payouts. Other revenue sources include game item purchases, cosmetic upgrades, and entry fees for competitions.
The initial token model relied entirely on the game's success and the continuous willingness of new players to buy 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 extending the utility of the token from AI Arena to the broader ARC platform. This evolution transforms NRN from a game-specific token into a platform token. In my view, this is a very positive shift.
The three new demand drivers for NRN include:
Revenue from ARC integration. Game companies that integrate ARC will generate revenue for the treasury through integration fees and ongoing royalties tied to game performance. Treasury funds can be used for NRN buybacks, ecosystem development, and incentivizing players on the trainer platform.
Trainer market fees. NRN derives value from the fees charged in the trainer market, where players can 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 increase accordingly.
Especially exciting are the earnings for gaming companies. This marks a shift from purely B2C models to a hybrid B2C and B2B model, creating a sustained influx of external capital into the NRN economy. As ARC has a broader target market, this revenue stream will exceed what AI Arena can generate.
The fees from the trainer market present a promising opportunity, but it depends on whether the ecosystem can achieve 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 reflective 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 dissipate.
The potential for network effects is enormous: more games → more players → more games joining → more players. This virtuous cycle can position NRN as the core token in the Crypto AI gaming ecosystem.
7. The Mother of Game AI Models
What’s the outcome? ARC's advantage lies in its ability to promote a variety of game types over time, allowing them to 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 game dataset reaches critical quality, 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 may be too early to say, but in the age of AI where data is the new oil, the potential in this area is limitless.
8. Our Ideas
(1) NRN evolves into a platform game — token repricing
With the launch of ARC and ARC RL, the project is no longer just a game company for a single product; it is now positioning itself as a platform and an AI gaming entity. This shift should lead to a repricing of the NRN token, which had been limited 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 diversified foundation for NRN's utility and value.
(2) Success is closely related to game partnerships
ARC's business model ties its success to the companies it partners with, as revenue streams are based on token distributions (in Web3 games) and payments of game royalties. Games closely linked to it are worth watching.
If the ARC game achieves tremendous success, the resulting value will flow back to NRN holders. Conversely, if the partnered games struggle, the flow of value will be restricted.
(3) Anticipating more integration with Web3 games
The ARC platform is well-suited for Web3 games, where competitive gameplay with incentive mechanisms seamlessly integrates with existing token economics.
By integrating ARC, Web3 games can immediately enter the 'AI agent' narrative. ARC RL brings the community together, motivating them to work toward common goals. This also opens up new opportunities for innovative mechanisms, like making activities such as 'games to airdrops' more appealing to players. By combining AI and token incentives, ARC adds depth and excitement that traditional games cannot replicate.
(4) AI gameplay has a learning curve
AI gameplay has a steep learning curve, which may create friction for new players. I spent an hour figuring out how to properly train my players in AI Arena.
However, the player experience friction in ARC RL is lower because AI training is handled in the backend as players play the game and submit data. Another unresolved question is how players feel when they know their opponent is AI. Does it affect them? Does it enhance or detract from the gaming experience? Only time will tell.
9. A Bright Future
AI will unlock brand new breakthrough 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 — providing an innovative approach to tackle player liquidity challenges without relying on unsustainable token economics.
The transition from game to mature platform is a huge leap for ARC. It not only opens up greater opportunities through partnerships with game companies but also reconfigures the integration of AI with gaming.
With its improved token economics and the potential for strong network effects, ARC's bright path seems just to have begun.
