📌 Token Distribution Models Summary
Token distribution models are methods used to decide how digital tokens are given out to participants in a blockchain or cryptocurrency project. These models outline who gets tokens, how many they receive, and when they are distributed. Common approaches include airdrops, sales, mining rewards, or allocations for team members and investors. The chosen model can affect the fairness, security, and long-term success of a project.
🙋🏻♂️ Explain Token Distribution Models Simply
Imagine a group of friends baking a cake together and deciding how to share it. Token distribution models are like the different ways they could slice and hand out the cake, such as giving everyone an equal piece or rewarding those who helped the most. The way the cake is divided can influence how happy everyone is and whether they want to bake together again.
📅 How Can it be used?
A project could use a token distribution model to reward early adopters and contributors while ensuring fair access for new users.
🗺️ Real World Examples
The Ethereum network used an initial coin offering (ICO) to distribute its Ether tokens. Early supporters could buy tokens before the network launched, helping raise funds for development while distributing tokens widely among users and investors.
Uniswap, a decentralised exchange, distributed its UNI governance tokens through an airdrop to anyone who had used the platform before a certain date, rewarding early users and encouraging ongoing community participation.
✅ FAQ
📚 Categories
🔗 External Reference Links
Token Distribution Models link
👏 Was This Helpful?
If this page helped you, please consider giving us a linkback or share on social media!
📎 https://www.efficiencyai.co.uk/knowledge_card/token-distribution-models-2
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
AI for Digital Transformation
AI for digital transformation refers to using artificial intelligence technologies to improve or change how organisations operate and deliver value. This can involve automating tasks, improving decision making, and creating new digital services. AI can help businesses become more efficient, responsive, and innovative by analysing data, predicting trends, and supporting better processes.
Telehealth Platforms
Telehealth platforms are digital systems that allow patients and healthcare professionals to connect remotely using computers, smartphones or tablets. These platforms often support video calls, messaging, appointment scheduling and sharing of medical records. By using telehealth, people can access medical advice and care from home or other convenient locations, reducing the need to travel to clinics or hospitals.
Continual Learning Metrics
Continual learning metrics are methods used to measure how well a machine learning model can learn new information over time without forgetting what it has previously learned. These metrics help researchers and developers understand if a model can retain old knowledge while adapting to new tasks or data. They are essential for evaluating the effectiveness of algorithms designed for lifelong or incremental learning.
Model Inference Frameworks
Model inference frameworks are software tools or libraries that help run trained machine learning models to make predictions on new data. They handle tasks like loading the model, preparing input data, running the calculations, and returning results. These frameworks are designed to be efficient and work across different hardware, such as CPUs, GPUs, or mobile devices.
AI for Aviation
AI for Aviation refers to the use of artificial intelligence technologies to improve various aspects of air travel and aircraft operations. This can include automating flight planning, enhancing safety through predictive maintenance, and optimising air traffic control systems. AI helps airlines and airports run more efficiently, reduce costs, and increase safety for passengers and crew.