A key value proposition for vertical AI applications is being model-agnostic. They act as a strategic layer for enterprises, allowing them to route tasks to the best available LLM at any given time. This de-risks enterprise AI strategy from being locked into a single model provider whose performance may be surpassed.

The "AI wrapper" concern is mitigated by a multi-model strategy. A startup can integrate the best models from various providers for different tasks, creating a superior product. A platform like OpenAI is incentivized to only use its own models, creating a durable advantage for the startup.

A successful strategy for AI startups is to initially leverage state-of-the-art foundation models to acquire users and data. Once sufficient high-quality, domain-specific data is collected, they can train their own specialized models to drastically cut costs and latency.

In the fast-changing AI landscape, standardizing on a single tool is a mistake. Monumental's CPO encourages his team to use various tools (Cursor, Devon, Claude) based on their needs. The strategy is to explicitly avoid dependency on any one platform, ensuring flexibility as new, better technologies emerge.

Enterprises will shift from relying on a single large language model to using orchestration platforms. These platforms will allow them to 'hot swap' various models—including smaller, specialized ones—for different tasks within a single system, optimizing for performance, cost, and use case without being locked into one provider.

The initial AI rush for every company to build proprietary models is over. The new winning strategy, seen with firms like Adobe, is to leverage existing product distribution by integrating multiple best-in-class third-party models, enabling faster and more powerful user experiences.

Like Kayak for flights, being a model aggregator provides superior value to users who want access to the best tool for a specific job. Big tech companies are restricted to their own models, creating an opportunity for startups to win by offering a 'single pane of glass' across all available models.

Instead of building its own models, Razer's strategy is to be model-agnostic. It selects different best-in-class LLMs for specific use cases (Grok for conversation, ChatGPT for reasoning) and focuses its R&D on the integration layer that provides context and persistence.

With new foundation models launching constantly, end-users don't care about the specific model name. A durable AI application should be model-agnostic, using an intelligent agent to select the best model for a given task. This focuses the product on the user's desired outcome, not the underlying tech.