NFT and Gaming: Chainlink Use Cases


09 Feb 2024
NFT and Gaming: Chainlink Use Cases

Enter Chainlink, a decentralized oracle network that plays a pivotal role in bridging the gap between blockchain smart contracts and real-world data. Its relevance to gaming and NFTs is profound, particularly through its Verifiable Randomness Function (VRF). Chainlink's VRF brings a new level of integrity and fairness to the process of generating in-game items and NFTs, ensuring that the rarity and uniqueness of these assets are genuinely random and tamper-proof.

MUST READ: "What is Chainlink"

Understanding NFTs in Gaming

NFT Chainlink

Explanation of NFTs and Their Unique Properties

NFTs, or Non-Fungible Tokens, represent uniquely identifiable assets that are verified on a blockchain. NFTs are distinct, with each token having a unique set of attributes and value. This uniqueness and the ability to prove ownership securely make NFTs particularly appealing for the gaming industry, where they can represent anything from in-game items and collectibles to characters and virtual land.

The Significance of NFTs in Gaming for Creating Rare and Unique In-Game Items

In gaming, NFTs bring fresh opportunities for both players and developers. Players gain genuine ownership of in-game assets, enabling trade, sale, or use across various games and platforms. Developers find new paths in game design, engagement, and monetization. Crafting rare and unique NFT items boosts the gaming experience, fosters community, and allows players to gain real-world value from gameplay.

Chainlink's Role in Enhancing NFT Rarity and Value

Overview of Chainlink Verifiable Randomness Function (VRF) and Its Importance

Chainlink VRF revolutionizes blockchain with secure, verifiable randomness, crucial for gaming and NFT minting. Its generated randomness is blockchain-verifiable, allowing independent audits to confirm its fairness and lack of external influence.

How Chainlink VRF Ensures the Fair Minting of Rare NFTs

For the gaming industry, Chainlink VRF ensures fair and transparent NFT minting. It helps determine the attributes and rarity of new NFTs, like character skins or weapons, guaranteeing equal chances for players to get rare items. This builds trust in the gaming community and boosts NFT value, as players trust the fairness of item acquisition.

Chainlink VRF: Revolutionizing Gaming Randomness

Chainlink VRF Applications in Gaming

Chainlink's Verifiable Random Function (VRF) has emerged as a cornerstone technology for blockchain-based applications, particularly in the gaming sector, where randomness plays a critical role in various aspects ranging from character creation to in-game dynamics and rewards distribution.

Detailed Explanation of What Chainlink VRF Is and How It Works

Chainlink VRF combines block data that is still unknown when the request is made with the oracle node’s pre-committed private key to generate both a random number and a cryptographic proof. The VRF's smart contract will only accept the random number input if it has valid cryptographic proof, and the cryptographic proof can only be generated if the VRF process is tamper-proof. This ensures the randomness is provable and not manipulated, bringing fairness and transparency to the forefront of digital randomness applications.

Examples of Gaming Applications Utilizing Chainlink VRF for Randomness

Case Studies:

  • Aavegotchi. This blockchain game integrates Chainlink VRF to mint rare NFTs called "Aavegotchis," each with randomly selected attributes when a player opens a Portal. This process ensures the rarity and uniqueness of each Aavegotchi, making the game more engaging and the assets more valuable.
  • Ether Legends. This digital collectible card game leverages Chainlink VRF to distribute rare crypto-backed NFT prizes to players. The randomness ensures fairness in awarding these prizes, making competitions more exciting and rewarding.
  • Axie Infinity. Known for its vibrant digital pet universe, Axie Infinity uses Chainlink VRF to generate random traits for Origin Axies. This randomness adds a layer of unpredictability and fairness to the breeding and battling mechanics within the game.

The Advent of Dynamic NFTs

Dynamic NFTs represent a groundbreaking shift in the NFT landscape, offering assets that can evolve over time based on real-world events, player achievements, or other criteria.

MUST READ: "What is Dynamic NFT"

Introduction to Dynamic NFTs and Their Evolving Nature

Unlike traditional NFTs, which are static and unchanging, dynamic NFTs can alter in rarity, appearance, or utility. This is made possible by smart contracts that can update the NFT's attributes in response to external data inputs or on-chain events, facilitated by oracles like Chainlink.

Examples of Dynamic NFTs in Sports:

  • MLB star Trey Mancini and NBA Rookie LaMelo Ball have both launched dynamic NFTs that change based on real-life performances and achievements. These NFTs not only serve as digital collectibles but also as living records of the athletes' careers, engaging fans in a novel and interactive manner.

GameFi and Chainlink

Chainlink in Gaming

The fusion of decentralized finance (DeFi) and gaming, known as GameFi, creates a new realm where players can earn real economic rewards through gameplay.

Exploring the Intersection of Gaming and DeFi (GameFi)

Chainlink supports the growing gaming ecosystem in several ways. It provides reliable data feeds for managing in-game economies. It also offers secure random number generation to ensure fair gameplay. Additionally, Chainlink automates smart contract executions, streamlining decentralized gaming operations.

No-Loss Savings Games

A notable DeFi innovation in gaming is no-loss savings games. These games blend entertainment with financial growth opportunities.

PoolTogether as an Example

PoolTogether is a platform that illustrates this concept. It uses Chainlink VRF to randomly select winners in its no-loss savings game. In this game, users pool their funds to collectively earn interest. One lucky participant wins the accumulated interest. Meanwhile, all other players receive their initial deposits back. Chainlink's secure randomness drives this model, promoting transparency and fairness. This encourages broader participation.

Chainlink in Sports and Esports Betting

Blockchain technology enhances sports and esports betting with transparency and fairness, thanks to decentralized oracles like Chainlink. These oracles securely bring real-world data to the blockchain, essential for settling bets on actual game outcomes.

Key Takeaways

Chainlink Gaming NFTs
  • Chainlink's Impact on Gaming and NFTs: Chainlink's technology, especially its Verifiable Randomness Function (VRF) and oracle services, has significantly impacted the gaming and NFT sectors by ensuring fairness, transparency, and trust in digital randomness and real-world data integration.
  • Future Potential of Chainlink in the Gaming Industry: The potential for Chainlink to revolutionize the gaming industry extends into areas like dynamic NFTs, GameFi, and decentralized finance applications within gaming ecosystems.


The transformative potential of Chainlink's technology in gaming and related sectors like NFTs and betting is profound. By enabling fair and transparent randomness, verifiable real-world data integration, and dynamic asset capabilities, Chainlink is not just enhancing existing gaming and betting ecosystems but also paving the way for entirely new gaming paradigms. As the landscape of blockchain gaming and NFTs continues to evolve, Chainlink's contributions are foundational to its growth and sustainability.


How does Chainlink's Verifiable Randomness Function (VRF) enhance the gaming and NFT sectors?

  • Chainlink's VRF ensures fairness and transparency in generating in-game items and NFTs by providing genuinely random and tamper-proof rarity and uniqueness.

What are dynamic NFTs and how do they differ from traditional NFTs?

  • Dynamic NFTs can evolve over time based on real-world events or player achievements, offering a more interactive and engaging experience compared to static traditional NFTs.

What integration challenges exist with Chainlink?

  • Issues like scalability and adoption with traditional platforms.

How does Chainlink protect NFT transactions?

  • Through secure data handling and fraud prevention mechanisms.

More about this Topic on Nextrope Blog

  1. What is Chainlink?
  2. Chainlink vs Polkadot
  3. Chainlink in DeFi: Use Cases
  4. Chainlink vs. Avalanche: Exploring the Blockchain Frontier
  5. Authorization and Identity: Chainlink Use Cases
  6. Chainlink and On-Chain Finance Use Cases


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Applying Game Theory in Token Design

Kajetan Olas

16 Apr 2024
Applying Game Theory in Token Design

Blockchain technology allows for aligning incentives among network participants by rewarding desired behaviors with tokens.
But there is more to it than simply fostering cooperation. Game theory allows for designing incentive-machines that can't be turned-off and resemble artificial life.

Emergent Optimization

Game theory provides a robust framework for analyzing strategic interactions with mathematical models, which is particularly useful in blockchain environments where multiple stakeholders interact within a set of predefined rules. By applying this framework to token systems, developers can design systems that influence the emergent behaviors of network participants. This ensures the stability and effectiveness of the ecosystem.

Bonding Curves

Bonding curves are tool used in token design to manage the relationship between price and token supply predictably. Essentially, a bonding curve is a mathematical curve that defines the price of a token based on its supply. The more tokens that are bought, the higher the price climbs, and vice versa. This model incentivizes early adoption and can help stabilize a token’s economy over time.

For example, a bonding curve could be designed to slow down price increases after certain milestones are reached, thus preventing speculative bubbles and encouraging steadier, more organic growth.

The Case of Bitcoin

Bitcoin’s design incorporates game theory, most notably through its consensus mechanism of proof-of-work (PoW). Its reward function optimizes for security (hashrate) by optimizing for maximum electricity usage. Therefore, optimizing for its legitimate goal of being secure also inadvertently optimizes for corrupting natural environment. Another emergent outcome of PoW is the creation of mining pools, that increase centralization.

The Paperclip Maximizer and the dangers of blockchain economy

What’s the connection between AI from the story and decentralized economies? Blockchain-based incentive systems also can’t be turned off. This means that if we design an incentive system that optimizes towards a wrong objective, we might be unable to change it. Bitcoin critics argue that the PoW consensus mechanism optimizes toward destroying planet Earth.

Layer 2 Solutions

Layer 2 solutions are built on the understanding that the security provided by this core kernel of certainty can be used as an anchor. This anchor then supports additional economic mechanisms that operate off the blockchain, extending the utility of public blockchains like Ethereum. These mechanisms include state channels, sidechains, or plasma, each offering a way to conduct transactions off-chain while still being able to refer back to the anchored security of the main chain if necessary.

Conceptual Example of State Channels

State channels allow participants to perform numerous transactions off-chain, with the blockchain serving as a backstop in case of disputes or malfeasance.

Consider two players, Alice and Bob, who want to play a game of tic-tac-toe with stakes in Ethereum. The naive approach would be to interact directly with a smart contract for every move, which would be slow and costly. Instead, they can use a state channel for their game.

  1. Opening the Channel: They start by deploying a "Judge" smart contract on Ethereum, which holds the 1 ETH wager. The contract knows the rules of the game and the identities of the players.
  2. Playing the Game: Alice and Bob play the game off-chain by signing each move as transactions, which are exchanged directly between them but not broadcast to the blockchain. Each transaction includes a nonce to ensure moves are kept in order.
  3. Closing the Channel: When the game ends, the final state (i.e., the sequence of moves) is sent to the Judge contract, which pays out the wager to the winner after confirming both parties agree on the outcome.

A threat stronger than the execution

If Bob tries to cheat by submitting an old state where he was winning, Alice can challenge this during a dispute period by submitting a newer signed state. The Judge contract can verify the authenticity and order of these states due to the nonces, ensuring the integrity of the game. Thus, the mere threat of execution (submitting the state to the blockchain and having the fraud exposed) secures the off-chain interactions.

Game Theory in Practice

Understanding the application of game theory within blockchain and token ecosystems requires a structured approach to analyzing how stakeholders interact, defining possible actions they can take, and understanding the causal relationships within the system. This structured analysis helps in creating effective strategies that ensure the system operates as intended.

Stakeholder Analysis

Identifying Stakeholders

The first step in applying game theory effectively is identifying all relevant stakeholders within the ecosystem. This includes direct participants such as users, miners, and developers but also external entities like regulators, potential attackers, and partner organizations. Understanding who the stakeholders are and what their interests and capabilities are is crucial for predicting how they might interact within the system.

Stakeholders in blockchain development for systems engineering

Assessing Incentives and Capabilities

Each stakeholder has different motivations and resources at their disposal. For instance, miners are motivated by block rewards and transaction fees, while users seek fast, secure, and cheap transactions. Clearly defining these incentives helps in predicting how changes to the system’s rules and parameters might influence their behaviors.

Defining Action Space

Possible Actions

The action space encompasses all possible decisions or strategies stakeholders can employ in response to the ecosystem's dynamics. For example, a miner might choose to increase computational power, a user might decide to hold or sell tokens, and a developer might propose changes to the protocol.

Artonomus, Github

Constraints and Opportunities

Understanding the constraints (such as economic costs, technological limitations, and regulatory frameworks) and opportunities (such as new technological advancements or changes in market demand) within which these actions take place is vital. This helps in modeling potential strategies stakeholders might adopt.

Artonomus, Github

Causal Relationships Diagram

Mapping Interactions

Creating a diagram that represents the causal relationships between different actions and outcomes within the ecosystem can illuminate how complex interactions unfold. This diagram helps in identifying which variables influence others and how they do so, making it easier to predict the outcomes of certain actions.

Artonomus, Github

Analyzing Impact

By examining the causal relationships, developers and system designers can identify critical leverage points where small changes could have significant impacts. This analysis is crucial for enhancing system stability and ensuring its efficiency.

Feedback Loops

Understanding feedback loops within a blockchain ecosystem is critical as they can significantly amplify or mitigate the effects of changes within the system. These loops can reinforce or counteract trends, leading to rapid growth or decline.

Reinforcing Loops

Reinforcing loops are feedback mechanisms that amplify the effects of a trend or action. For example, increased adoption of a blockchain platform can lead to more developers creating applications on it, which in turn leads to further adoption. This positive feedback loop can drive rapid growth and success.

Death Spiral

Conversely, a death spiral is a type of reinforcing loop that leads to negative outcomes. An example might be the increasing cost of transaction fees leading to decreased usage of the blockchain, which reduces the incentive for miners to secure the network, further decreasing system performance and user adoption. Identifying potential death spirals early is crucial for maintaining the ecosystem's health.

The Death Spiral: How Terra's Algorithmic Stablecoin Came Crashing Down
the-death-spiral-how-terras-algorithmic-stablecoin-came-crashing-down/, Forbes


The fundamental advantage of token-based systems is being able to reward desired behavior. To capitalize on that possibility, token engineers put careful attention into optimization and designing incentives for long-term growth.


  1. What does game theory contribute to blockchain token design?
    • Game theory optimizes blockchain ecosystems by structuring incentives that reward desired behavior.
  2. How do bonding curves apply game theory to improve token economics?
    • Bonding curves set token pricing that adjusts with supply changes, strategically incentivizing early purchases and penalizing speculation.
  3. What benefits do Layer 2 solutions provide in the context of game theory?
    • Layer 2 solutions leverage game theory, by creating systems where the threat of reporting fraudulent behavior ensures honest participation.

Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.


Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics


The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.


What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.