Modern AI systems face two main types of errors that prevent autonomous operation: hallucination and bias. Hallucination represents precision errors where the model produces inconsistent output, while bias manifests as systematic deviations from the underlying truth. The current error rate remains too high for AI to operate independently in critical scenarios, creating a fundamental gap between AI's theoretical capabilities and practical applications.
2. Training Dilemma
AI model creators face an impossible choice: curating training data to reduce hallucinations inevitably introduces bias through selection criteria, while training on diverse data sources to minimize bias leads to increased hallucinations. This creates an unchangeable boundary in AI performance where no single model can minimize both types of errors simultaneously, regardless of scale or architecture.
3. Limitations of Centralization
Simply combining several models under centralized control cannot solve the reliability challenges because the selection of the models themselves introduces systematic errors. Centralized curatorial choices undoubtedly reflect certain perspectives and limitations, while many truths are essentially contextual across different cultures, regions, and domains.