For services like Secretary.com, the defensible moat isn't the AI model itself but the unique dataset generated by human oversight. This data captures the nuanced, intuitive reasoning of an expert (like an EA handling a complex schedule change), which is absent from public training data and difficult for competitors to replicate.

As AI and better tools commoditize software creation, traditional technology moats are shrinking. The new defensible advantages are forms of liquidity: aggregated data, marketplace activity, or social interactions. These network effects are harder for competitors to replicate than code or features.

CEOs of platforms like ZocDoc and TaskRabbit are not worried about AI agent disruption. They believe the immense complexity of managing their real-world networks—like integrating with chaotic healthcare systems or vetting thousands of workers—is a defensible moat that pure software agents cannot easily replicate, giving them leverage over AI companies.

The long-held belief that a complex codebase provides a durable competitive advantage is becoming obsolete due to AI. As software becomes easier to replicate, defensibility shifts away from the technology itself and back toward classic business moats like network effects, brand reputation, and deep industry integration.

Amdahl's Law states that when you speed up one part of a process, the un-optimized parts become the new bottleneck. In business, as AI automates tasks like coding, previously overlooked advantages (e.g., human relationships, institutional knowledge) become the new, more critical moats.

The term "unsloppable" describes companies whose competitive advantage isn't their codebase, which AI can replicate. Instead, their strength comes from durable moats like hardware, strong network effects (Uber), exclusive IP (Disney), or physical infrastructure, which are difficult for AI-powered startups to clone.

As AI commoditizes software, the most defensible businesses are no longer asset-light SaaS models. Instead, companies with physical world operations, regulatory moats, and liability are safer investments. Their operational complexity, once a weakness, now serves as a formidable barrier against pure AI-driven disruption.

Oren Zeev argues against the narrative that AI will kill all incumbents. He believes businesses with operational complexity, deep data moats, and strong distribution are not easily disrupted. These companies are more likely to leverage AI to their advantage, while simpler software companies are at greater risk.

As AI accelerates technological progress and shortens relevance cycles, traditional tech moats become less defensible. However, network effects—especially in complex, fragmented marketplaces—remain a powerful and durable advantage. An AI agent cannot be simply prompted to "create a network effect."