AI commands premium valuations, but many "AI companies" have limited genuine AI capabilities. Separating substance from hype is critical for accurate valuation.
AI Reality Check
What Counts as AI?
- Machine learning models making predictions
- Natural language processing
- Computer vision systems
- Recommendation engines
- Autonomous decision-making
What Doesn't Count
- Rule-based systems marketed as AI
- Simple analytics dashboards
- APIs to third-party AI services
- "AI-ready" infrastructure
- Future AI roadmap without current capability
AI Valuation Factors
Value Drivers
- Proprietary models: Custom models with competitive advantage
- Training data: Unique data assets for model training
- Production ML: Models deployed and generating value
- MLOps maturity: Ability to iterate and improve
- Team expertise: ML engineering and data science talent
Value Detractors
- Models trained on public data
- Commodity models available from cloud providers
- No clear path from research to production
- High annotation/labeling costs
- Regulatory restrictions on AI use
Assessment Questions
- What specific AI models are in production?
- What is the model performance vs. baselines?
- What data was used for training and who owns it?
- How are models monitored and retrained?
- What would it cost to replicate the capability?
Key Takeaway: AI premium valuations require AI substance. Validate claims with technical evidence, not marketing materials.
