Big Models, Bigger Bills: Why Training LLMs Just Doesn’t Add Up for Most Enterprises

Summary

This August 2025 post unpacks the economic and practical realities behind training large language models (LLMs) inside traditional enterprises. It argues that most organizations lack the data, scale, and sustained value proposition to justify the cost of custom LLMs. Instead, companies are seeing better ROI with fine-tuned small models, hybrid AI stacks, and task-specific agents. The piece uses DeepSeek as a case study of smart trade-offs and introduces the concept of a "boutique AI renaissance" — a move away from size and toward smart, contextual AI.

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