Large language models (LLMs) dominate headlines for their impressive capabilities, like passing exams and winning competitions, but they aren’t the primary drivers of business efficiency. Executives at companies using AI daily report that smaller, simpler models are actually the backbone of their operations. These models are faster, cheaper, and more specialized, handling most tasks with greater effectiveness than their larger counterparts. Kyle Lo, a research scientist at the Allen Institute for AI, emphasizes that many business needs don’t require the complexity of LLMs. Instead, organizations are building AI systems like assembly lines, where data flows in and actionable insights or products emerge on the other end. Smaller models perform specific functions efficiently, allowing companies to streamline workflows and reduce costs. This shift highlights a growing recognition that practical applications of AI often rely on models that may not attract the spotlight but are essential for day-to-day operations.