AI-native workflows have a moat problem
The article argues that AI-native workflows built on large language models lack sustainable competitive advantages (moats) because the underlying models are easily replicated or replaced, and the value is captured more by model providers than by the application layer businesses that rely on them.
Background
- "Moat" is a business term popularized by Warren Buffett; it refers to a company's durable competitive advantage that protects it from competitors (e.g., brand, network effects, proprietary tech, high switching costs).<br>- A "workflow" is a sequence of steps to complete a task; an "AI-native workflow" means a process designed from scratch around AI capabilities (e.g., automated document analysis, sales lead scoring), rather than bolting AI onto an existing manual process.<br>- The core problem: AI-native workflows are often easy for competitors to copy because the underlying AI models (like GPT, Claude, Gemini) are available to everyone (commoditized), and the workflow logic itself is typically straightforward to replicate — meaning companies struggle to build a defensible moat around them.<br>- This is a hot topic in startup and tech strategy: venture capitalists and founders debate whether AI companies can sustain long-term profits or will face intense margin compression.