Fundamental versus contingent nature of ML pipeline market segmentation

Ascertain whether the observed segmentation and distinct pricing dynamics across pre‑training, fine‑tuning, and inference stages are fundamental features of AI production or artifacts of current technology.

Background

The paper describes how data plays different roles across the ML pipeline: pre-training (large-scale public datasets), post-training/fine-tuning (high-quality curated datasets), and inference (continuous feedback). These differences currently manifest in distinct market structures and pricing.

Determining whether these differences are intrinsic to AI production or contingent on current technological implementations would guide market design and policy interventions.