economic-pressure-drives-capability-cost-decoupling

IN derived (depth 3)

Created 2026-06-21T13:06:41+00:00 · Reviewed 2026-06-21T14:41:08+00:00

Exponential training cost scaling across model generations creates economic pressure that drives architectural innovations systematically decoupling capability from cost: MoE decouples parameter count from inference compute, knowledge distillation decouples model size from output quality, and compute routing selects granularity per task — all validated across the full scale spectrum from 110M to 2T parameters.

Justifications

SL — Exponential cost growth creates the selection pressure; MoE, distillation, and routing are the adaptive responses

Antecedents (all must be IN):

  • IN training-costs-scaled-exponentially-across-generations — Training costs scaled by orders of magnitude — from ~$500 for BERT (4 TPUs, 4 days) through ~$50K for GPT-2 to millions for PaLM — while the per-parameter cost formula (6 FLOPs per parameter per token) remained consistent, meaning cost growth is driven by scale ambition, not inefficiency.
  • IN distillation-and-routing-jointly-decouple-capability-from-cost — Knowledge distillation (validated across the full scale spectrum, from 110M to 2T parameters) and multi-granularity compute routing (intra-model MoE and inter-model task routing) together decouple capability from inference cost along two independent axes: distillation compresses capability into smaller models, while routing allocates compute only where needed — together enabling deployment of frontier-class capability at a fraction of nominal cost.

Dependents

These beliefs depend on this one: