Relying on a single frontier model is risky and inefficient. The next phase of AI will involve intelligently routing queries to the most appropriate model—be it cheaper, faster, or local. This will redistribute value from a few dominant labs to a long tail of specialized models, maturing the ecosystem.

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.

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.

Companies are discovering they're overpaying for AI by using powerful models for mundane tasks. They will increasingly adopt routers that intelligently direct queries to the most cost-effective model. This move will drive down costs and commoditize the AI model layer.

Companies like Meta and Ramp are building AI routers to automatically send simple tasks to cheaper models. This trend shows the enterprise AI market is maturing past a 'one-model-fits-all' approach, focusing instead on cost management and operational efficiency by treating models as a commodity portfolio.

The greatest value in AI won't be captured by frontier labs alone. Instead, companies in the "applied layer" are incentivized to build routing systems that use expensive frontier models for high-level orchestration while deploying cheaper open-source models for bulk tasks, creating a more efficient, barbell-shaped cost structure.

Enterprises will not lock into a single AI provider. The winning strategy involves using a strong open-source base model, fine-tuning it with proprietary data to create a custom model, and using a router to transparently leverage multiple frontier models for specific tasks.