🔮KnowledgeFi

Igniting A Movement

KnowledgeFi describes a state where all AI value creators, starting with Knowledge Asset owners, can participate and get a fair share of economic benefits in the AI-driven future. KIP Protocol was designed to bring about this state.

Whether the specific interface of a given AI app makes this clear or not, AI works on a pay-per-query model, with monthly subscriptions being a reflection of the average monthly queries per user (usually API subscriptions make this clear, while web-client users pay a standard monthly fee).

As users make queries to an AI app, credits are spent as the the app interacts with AI models and Knowledge Assets to fulfill the query. These credits are an economic representation of used GPU computing power, plus the other costs and margins of the model developer/provider. Compute providers, model designers, and shareholders all get a share of these revenues, but the providers of the data upon which the entire edifice is built do not.

We are building a Web3 protocol in which all AI stakeholders and value creators like Knowledge Asset owners can interact and exchange fair economic value when serving AI users.

We call this KnowledgeFi.

KnowledgeFi: A Free & Fair Knowledge Economy in the AI Age

Our goal is to empower everyone, through the systematic establishment of true digital property rights, to unlock the full economic value of their Knowledge Assets.

Once ownership rights over the Knowledge Assets are secured, whole new business models centering around the trade of knowledge and data become possible.

Overview of Knowledge Asset Revenue Flow

Revenue Split under Retrieval Augmented Generation (RAG)

Some Use Cases:

Personal Medical Data	AI diagnostic apps can use confidential personal medical data, including medical history, genetics, and lifestyle, to provide customized diagnoses and treatment plans, enhancing treatment effectiveness and efficiency.
Educational Content and Curricula:	Educators can tokenise text books and curricula, which can be used to train AI models in personalized learning platforms or as a knowledge base for educational queries. For example, a university professor, with his published textbook vectorized in a Knowledge Base can link it to an LLM to allow students to search for AI-powered answers augmented with the knowledge in his text book. If he creates his own AI app and links it to that KB, he can monetize queries made to his KB.
Medical Research Data:	Medical researchers can turn clinical trial results and research findings into training data. These can be used as training data for AI models in healthcare, assisting in drug discovery or diagnostics.
Specialised Research:	An expert researcher, with highly curated research on a topic, can sell, lease, charge for use of his research by anyone wishing to augment their AI with his expertise.
Financial Market Analysis:	Financial analysts can tokenize historical market data and analysis. This data serves as a rich training ground for AI models in predictive analytics for stock market trends and investment strategies.
Secure De-Sci	Data authenticity can be shared and used to conduct statistical and machine learning analyses without compromising anonymity.
Legal Case Databases	Legal professionals train models on their own prior case notes, helping in legal research or predicting outcomes.
Consumer Behavior Data:	Market researchers can use detailed consumer behavior data to train AI models in fields like targeted marketing, product development, and consumer trend analysis.
Logistics and Predictive Transport Information	Municipalities, delivery corporations, and national transport systems can contribute data to spur the creation of larger and more complete logistics and transport models without any risk of allowing sensitive commercial or security information to leak.
Retail Sales Data:	Retailers work together to train models on detailed sales data, customer preferences, and shopping trends, without allowing competitors access to detailed commercial data that they may wish to keep private, allowing better inventory management for all parties with no security risks. Businesses can thus tokenize their supply chain and logistics data, crucial for AI models focusing on optimizing logistics, forecasting demand, and managing inventory.
Art and Design Portfolios:	Artists and designers can tokenize their artwork and design portfolios, allowing fans and corporates to buy access to their unique style for a fee, rather than - as is currently the case - training local models on pirated work.
Music and Audio Libraries:	Musicians can tokenize their music libraries and compositions, useful for training AI in music recommendation systems or creating new compositions.
Cybersecurity Threat Data:	Cybersecurity firms can tokenize databases of threat intelligence and security incidents, allowing the development of accurate AI threat detection and security protocol optimization models without any need to share detailed descriptions of specific vulnerabilities and exploits.

This is the AI future we want. A future in which more people are incentivised to contribute to AI development, where knowledge is not hoarded for fear of big tech LLM scraping, but rather shared fairly to generate even more economic value.

This is KnowledgeFi.