A Theory of Training Profit-Optimal LLMs
Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in hand with massive amounts of capital expenditure. While it is established that scaling up LLMs reliably increases model quality (quantified in terms of loss or downstream evaluations), it is unclear how these quality improvements translate to potential revenue, and whether revenue increases would offset costs of larger-scale training and inference. In this work, we develop an economic model for characterizing the rational behavior of an LLM training firm by combining scaling laws with microeconomic theory. Under our model of firm behavior, LLM quality can be increased with more parameters...
| Authors | Sophie Hao, William Merrill |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 11 June 2026 |
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