User stickiness for AI models is increasingly driven by the 'harness'—the custom prompts, workflows, and integrations built around a specific model. This ecosystem creates high switching costs, even when a competing model offers incrementally better performance.

As AI commoditizes user interfaces, enduring value will reside in the backend systems that are the authoritative source of data (e.g., payroll, financial records). These 'systems of record' are sticky due to regulation, business process integration, and high switching costs.

AI models are commoditized, but the ecosystem of tools, services, and compliance standards is increasingly complex. The example of needing nine Azure services for only 39% NIST compliance highlights this. Companies offering a consolidated, simplified path to value will hold a significant competitive advantage.

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.

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."

AI capabilities offer strong differentiation against human alternatives. However, this is not a sustainable moat against competitors who can use the same AI models. Lasting defensibility still comes from traditional moats like workflow integration and network effects.

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.

As AI makes software development nearly free, traditional engineering moats are disappearing. Businesses must now rely on durable advantages like network effects, economies of scale, brand trust, and defensible IP to survive, becoming "unsloppable."

The enduring moat in the AI stack lies in what is hardest to replicate. Since building foundation models is significantly more difficult than building applications on top of them, the model layer is inherently more defensible and will naturally capture more value over time.