economic-pressure-drives-capability-cost-decoupling-v2

IN premise

Created 2026-08-24T18:41:34+00:00

Training costs have scaled by orders of magnitude across model generations (from ~$500 for BERT to millions for PaLM) while per-parameter compute requirements remained consistent, indicating growth is driven by scale ambition rather than inefficiency. In response, architectural approaches along two complementary axes decouple capability from inference cost: knowledge distillation compresses capability into smaller models (validated across the full scale spectrum from 110M to 2T parameters), and multi-granularity compute routing—encompassing intra-model MoE and inter-model task routing—allocates compute only where needed. Together, these mechanisms support deployment of frontier-class capability at a fraction of nominal inference cost.

Summary

Making bigger models has become vastly more expensive, but that growth reflects a deliberate push for scale rather than a failure of efficiency. The practical takeaway is that two well-validated techniques—compressing large models into smaller ones and routing compute only to the parts a task actually needs—let a system deliver frontier-level performance at a small fraction of what nominal inference costs would suggest.