The performance race in frontier AI models is irrelevant for most business use cases. The vast majority of enterprise AI traffic—an estimated 90%—will run on cheaper, older, or specialized open-source models that are sufficient for day-to-day operational tasks, rather than costly state-of-the-art ones.

The AI market isn't a zero-sum game between open and closed models. As specific use cases mature, companies will migrate them to cheaper, fine-tuned open-weight models for efficiency. Frontier closed models will then be reserved for orchestration or more complex tasks, allowing both ecosystems to grow exponentially.

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.

The era of using the most powerful AI model for every task is ending. Companies are now focused on the trade-off between quality, cost, and latency. The key question is no longer "Which model is best?" but "Which model is good enough for this task at the lowest price point?"

For most enterprise tasks, massive frontier models are overkill—a "bazooka to kill a fly." Smaller, domain-specific models are often more accurate for targeted use cases, significantly cheaper to run, and more secure. They focus on being the "best-in-class employee" for a specific task, not a generalist.

It's economically rational to use expensive, high-IQ frontier models for functions with unlimited upside, like sales or product development. For functions where the goal is precision rather than unbounded creativity (e.g., accurately closing financial books), cheaper, fine-tuned open-weight models are more efficient.

The AI model landscape isn't a simple ladder of best to worst. Instead, it's a "spiky" frontier where different models offer unique strengths. For example, one model may excel at complex, niche problems while another is faster, more affordable, and better for collaborative, general-purpose tasks, necessitating a multi-tool approach.

The critical new AI skill isn't just using the most powerful model, but discerning when a free, private local model is sufficient versus when an expensive cloud model is necessary. This model-to-task matching instinct separates amateurs from pros by optimizing for cost, speed, and privacy.

The smartest 'AI-pilled' companies adopt a two-tiered model strategy. They use expensive, frontier models for internal, high-leverage tasks like creating new knowledge and optimizing processes. However, they use cheaper, open-weight models in the 'bill of materials' for the customer-facing product to manage costs effectively.