Recognizing there is no single "best" LLM, AlphaSense built a system to test and deploy various models for different tasks. This allows them to optimize for performance and even stylistic preferences, using different models for their buy-side finance clients versus their corporate users.

The future of enterprise AI isn't choosing one provider. Instead, companies will use a "composable model" approach, routing queries to a combination of powerful frontier models and their own fine-tuned open-source models. This strategy, dubbed the "council of LLMs," optimizes for cost, performance, and specialization on proprietary data.

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.

As noted by Chamath Palihapitiya, businesses fear deploying major AI models directly, seeing it as letting the 'fox into the henhouse' where their usage data could train a future competitor. This creates a strategic opening for 'harness-first' companies that offer enterprises control and choice over underlying models.

Navan's CEO sees the debate over which LLM is best as unimportant because the infrastructure is becoming a commodity. The real value is created in the application layer. Navan's own agentic platform, Cognition, intelligently routes tasks to different models (OpenAI, Anthropic, Google) to get the best result for the job.

Cursor positions itself as a model-agnostic platform, turning potential competitors like OpenAI and Anthropic into partners. By being the "Snowflake for SDLC" on top of the "hyperscaler" models, they create a differentiated value layer focused on a vertical use case.

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.

Large enterprises are avoiding commitment to a single AI provider like OpenAI or Anthropic. Instead, they're building control planes and abstraction layers that allow them to hot-swap the underlying models, mitigating technology risk and preventing dependence on one provider's terms of service.

Perplexity's core advantage is its model-agnostic orchestration. Unlike vertically integrated competitors (Google, OpenAI), it can select the best model for any task—whether from GPT, Claude, or open-source alternatives—to offer a superior, specialized "orchestra" of AI capabilities.