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.

Contrary to the popular belief that open-source AI will inevitably catch up, a NIST analysis indicates the performance gap between open and closed-source models is growing. The performance trend lines are diverging, suggesting frontier models are improving at a significantly faster rate.

Glean's co-founder argues that most enterprise tasks don't require expensive frontier models. Open-source alternatives are now capable enough for the vast majority of use cases. The primary adoption driver has shifted from data privacy to pure cost savings, as enterprises seek to control skyrocketing AI bills.

Contrary to the popular narrative that open-source AI will quickly commoditize the market, there is evidence that the frontier is accelerating faster than the open-source community can keep up. This potential divergence challenges the 'good enough' argument and suggests that proprietary models may maintain a significant, defensible lead for longer than expected.

Regulatory uncertainty and delayed access to top-tier models from labs like OpenAI and Anthropic are pushing enterprises to adopt open-source alternatives like GLM 5.2. This shift allows companies to secure their own computing resources and train proprietary models, gaining data sovereignty and cost control.

Contrary to the belief that open-source models would quickly catch up, 2024 has shown the opposite. Frontier models are extending their lead, particularly in long-running tasks, which unlocks new enterprise use cases and allows them to capture the vast majority of revenue.

The market isn't a battle between proprietary frontier models and open-source alternatives. Instead, both are seeing parabolic growth. While open-source becomes more capable for simple tasks, the demand for cutting-edge capabilities unlocked by frontier models is also expanding rapidly, creating a positive-sum environment.

Open and closed source AI models will coexist by serving different parts of the market. Companies with core AI needs and large budgets will "build" on open source for control and customization. Most others will "buy" closed-source APIs for convenience, mirroring the established build-vs-buy dynamic for other technologies.

The fear that open source will erode the business of OpenAI and Anthropic is misplaced. As open source models make existing solutions cheaper, they compel frontier model providers to tackle the vast number of more complex, unsolved problems, effectively expanding the entire market.