LLM Scaling Hits Diminishing Returns, Echoing CPU GHz Race

The author argues that large language models face diminishing returns from scaling compute, data, and parameters, similar to the CPU clock speed race of the early 2000s. Costs rise, latency grows, and improvements become incremental. The future may lie in systems of specialized models with shared ontologies, rather than ever-larger monolithic models.