Wilkinson argues that the traditional moat for software—the high cost and difficulty of hiring programmers—has vanished. He compares it to a machine that makes perfect pizza cheaply: consumer quality rises, but business margins plummet. Lasting value must now come from other sources like brand or distribution.

Unlike the slow denial of SaaS by client-server companies, today's SaaS leaders (e.g., HubSpot, Notion) are rapidly integrating AI. They have an advantage due to vast proprietary data and existing distribution channels, making it harder for new AI-native startups to displace them. The old playbook of a slow incumbent may no longer apply.

When asked if AI commoditizes software, Bravo argues that durable moats aren't just code, which can be replicated. They are the deep understanding of customer processes and the ability to service them. This involves re-engineering organizations, not just deploying a product.

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.

According to Box CEO Aaron Levie, the stickiest SaaS products are those with strong network effects, deep integrations, and mission-critical workflows. A simple heuristic for vulnerability: if you can get the same value from a fresh install as a decade-old one, your product can be easily replaced by AI-generated software.

Creating a basic AI coding tool is easy. The defensible moat comes from building a vertically integrated platform with its own backend infrastructure like databases, user management, and integrations. This is extremely difficult for competitors to replicate, especially if they rely on third-party services like Superbase.

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.

Advanced AI tools have made writing software trivially easy, erasing the traditional moat of technical execution. The new differentiators for businesses are non-technical assets like brand trust, distribution networks, and community, as the software itself has become instantly replicable.

In enterprise AI, competitive advantage comes less from the underlying model and more from the surrounding software. Features like versioning, analytics, integrations, and orchestration systems are critical for enterprise adoption and create stickiness that models alone cannot.