As base AI models become commoditized, the key competitive advantage will be the unique, proprietary context an enterprise builds. This 'organizational brain,' composed of customer data, internal knowledge, and past learnings, will be more valuable than the plug-and-play model itself.

While AI models get the headlines, they are becoming commodities. The true competitive advantage lies in building a custom "harness"—the surrounding application, data integrations, and specialized tools that direct the model's power to solve a specific user problem effectively.

The most defensible AI companies don't just have superior models; they embed themselves deeply into customer workflows. The primary barrier to adoption is change management, so overcoming that hurdle creates a durable competitive advantage that is difficult to displace.

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.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

As powerful AI models become cheap and universally accessible, having one is no longer a defensible moat. The real, lasting advantage for a business now comes from assets that a better model can't easily replace: proprietary customer data, deeply integrated user workflows that are difficult to replicate, and long-term client relationships.

As foundational AI models become commoditized, the competitive advantage is no longer raw intelligence. Lasting value comes from building a reliable ecosystem around the AI, focusing on deep workflow integration, governance, user trust, and flawless operational execution. This is the true defensible moat.

The future of AI at work belongs to platforms with the richest shared business context, not just the best LLM. A proprietary data model like Asana's Work Graph, which maps goals and tasks, creates a compounding advantage by feeding AI agents the specific data needed to be effective and improve over time.

A complex "applied AI layer" is emerging as the source of durable value in enterprise AI. This goes beyond simple API calls to include model routing, bespoke workflow integration, and unique human-in-the-loop interfaces. Companies building this complex layer gain a defensible moat that thin wrappers on LLMs cannot replicate.