AI Data Problem

The rapid advancement of artificial intelligence (AI) has positioned it as a transformative force across industries, revolutionizing decision-making, automation, and knowledge creation. However, the existing AI landscape remains deeply centralized, dominated by a few monopolistic entities that control data, computing power, and model access. Only a handful of super companies have mastered large-scale model training and inference technologies.

Many challenges faced by large models, including hallucinations, stem from data scarcity. The supply of low-cost, publicly available internet data is nearing exhaustion, driving up the cost of data acquisition. Further challenges include the limited availability of personal and high-quality industry data, the difficulty of leveraging such data at scale while preserving privacy, the complexity of assessing data quality and effectiveness, and the lack of mechanisms for individuals and enterprises to receive fair compensation for their data. Going forward, the success of AI applications will largely hinge on how effectively data is generated and utilized.

In summary, LazAI seeks to address the following key challenges:

1. Data Sharing for AI utilization is Challenging: In the AI domain, data is a foundational asset; however, the sharing of personal and industry-specific data remains significantly constrained due to strong privacy and security concerns. Both individuals and organizations are often reluctant to share data for fear of misuse, unauthorized access, or potential fraud. Moreover, the absence of standardized protocols and robust AI infrastructure further hampers the efficient sharing and utilization of data within AI workflows, even when stakeholders are willing to collaborate.

2. Data Quality and Evaluation Are Challenging: The quality of publicly available data is highly inconsistent, and a large proportion of high-quality data is proprietary or protected by copyright, limiting access for developers aiming to train competitive AI models. Establishing a unified framework to evaluate data effectiveness across diverse scenarios and perspectives is inherently difficult. Furthermore, there is a lack of viable mechanisms to support personalized, utility-based data evaluation, thereby restricting data optimization and impeding the overall progress of AI systems.

3. Revenue Generation and Distribution is Difficult: Throughout the lifecycle of AI model development and deployment, data contributors and model developers struggle to obtain fair compensation due to insufficient transparency and lack of verifiability. Centralized AI platforms often function as opaque systems, making it difficult to trace how data is utilized and how its value is realized. Consequently, data owners lack the ability to assess their data’s contribution to model outcomes or to claim appropriate economic rewards.

Therefore, we aim to build a world where everyone has the opportunity to align AI with their own data, build personalized AI models at minimal cost, and share in the value generated by their data and models through alignment. To achieve this, Lazai is committed to delivering decentralized AI blockchain infrastructure, AI asset protocols, and workflow toolkits. By leveraging decentralized, user-owned data sources, Lazai empowers developers to build value-aligned AI agents.