To survive against subsidized tools from model providers like OpenAI and Anthropic, AI applications must avoid a price war. Instead, the winning strategy is to focus on superior product experience and serve as a neutral orchestration layer that allows users to choose the best underlying model.

As foundational AI models become more accessible, the key to winning the market is shifting from having the most advanced model to creating the best user experience. This "age of productization" means skilled product managers who can effectively package AI capabilities are becoming as crucial as the researchers themselves.

While many new AI tools excel at generating prototypes, a significant gap remains to make them production-ready. The key business opportunity and competitive moat lie in closing this gap—turning a generated concept into a full-stack, on-brand, deployable application. This is the 'last mile' problem.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

In SaaS, value was delivered through visible UI. With AI, this is inverted. The most critical, differentiating work happens in the invisible infrastructure—complex RAG systems and custom models. The UI becomes the smaller, easier part of the product, flipping the traditional value proposition.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

MLOps pipelines manage model deployment, but scaling AI requires a broader "AI Operating System." This system serves as a central governance and integration layer, ensuring every AI solution across the business inherits auditable data lineage, compliance, and standardized policies.

As foundational AI models become commoditized, the key differentiator is shifting from marginal improvements in model capability to superior user experience and productization. Companies that focus on polish, ease of use, and thoughtful integration will win, making product managers the new heroes of the AI race.

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.