For a long time, AI progress was measured by scale: bigger models, more data, and more computing power. While this approach yielded impressive capabilities, it also created challenges like high costs, slow performance, and difficulty in deployment. Distillation emerges as a crucial technique to address these issues, enabling the creation of smaller, specialized AI models for specific tasks and environments.

• AI progress has historically been driven by scale (bigger models, datasets, GPUs, parameters).
• Scale has led to highly capable AI but also created problems like expense, slowness, and centralization.
• Many real-world use cases do not require the largest general-purpose models.
• Distillation is becoming central to creating specialized, efficient, and practical AI solutions.
• Enterprises seek reliable competence over generic brilliance.