Executive Summary
Every pitch deck today seems to include AI. From "AI-powered" to "ML-driven," founders are racing to position their companies as AI-first. But for VCs, distinguishing between genuine AI innovation and marketing spin has become increasingly challenging. This guide cuts through the hype to help you evaluate AI claims with confidence.
The AI Landscape Has Changed
Then
Just two years ago, AI was a differentiator. Companies building real AI solutions stood out, and evaluating AI claims meant looking for deep technical expertise and proprietary algorithms. The barriers to entry were high, and true AI companies were rare.
Now
AI has become democratized. With powerful models available off the shelf and AI infrastructure increasingly commoditized, the question is no longer "Are they really using AI?" but rather "How are they using AI to create sustainable competitive advantage?"
Decoding AI Claims
When a founder claims their startup is "AI-powered," what does it really mean? Here's what to look for:
Level 1: AI as a Feature
- Uses standard APIs from OpenAI, Google, or other providers
- Minimal customization or training
- AI enhances existing functionality
- Limited competitive moat
Level 2: AI as a Product
- Custom models for specific use cases
- Proprietary training data
- Clear ROI from AI implementation
- Some technical differentiation
Level 3: AI as a Core Innovation
- Novel applications of AI
- Unique data advantages
- Significant technical barriers
- Clear network effects
