Private Evals

Custom, domain-specific evaluation frameworks developed by organizations to assess AI model performance on their specific use cases and requirements. satya-nadella identified private evals as a new form of "Token IP" that companies will develop as public benchmarks become insufficient for real-world assessment.

Core Concept

Beyond Public Benchmarks

Private evals address fundamental limitations of public evaluation frameworks:

• Domain Specificity: Tailored to specific business contexts and requirements
• Proprietary Tasks: Evaluating capabilities relevant to unique organizational needs
• Competitive Advantage: Assessment criteria that align with business differentiation
• Real-World Relevance: Metrics that correlate with actual business value creation

Token IP Formation

Private evals represent a new form of intellectual property:

• Evaluation Methodology: Proprietary frameworks for assessing AI capabilities
• Domain Expertise: Deep knowledge embedded in evaluation criteria
• Competitive Moat: Evaluation capabilities that competitors cannot easily replicate
• Business Intelligence: Understanding what truly matters for specific use cases

Industry Context

Public Benchmark Limitations

satya-nadella noted that public evaluations "all can be maxed," making them insufficient for:

• Differentiation: All major models perform similarly on standard benchmarks
• Practical Assessment: Benchmarks don't reflect real-world deployment complexity
• Gaming Concerns: Public benchmarks become optimization targets rather than true measures
• Context Specificity: Generic benchmarks miss domain-specific requirements

Enterprise Requirements

Organizations need evaluation frameworks that:

• Reflect their specific data types and formats