Tiered Super-Moore's Law: Price Evolution, Production Frontiers, and Market Competition in LLM Inference Services
Decomposes the ~600x token-price decline into TIER-SPECIFIC half-lives (economy 1.10yr, mid 1.55yr, flagship near-zero fit R2=0.031 from 31.5x reasoning premium); dates a May-2024 structural break (Chow F=5.74, p=0.005) from technology- to competition-driven decline; attributes ~103.7% of cost reduction to TFP vs -0.9% from GPU hardware — a direct empirical counter to the memory/hardware-centric cost story. MEASURED (OpenRouter 318 + Epoch 3,237 models + 62 milestones); econometric decomposition ESTIMATED.
Tiered Super-Moore's Law
What this is
The most rigorous token-price-trend + market-structure paper the KB has seen (2026-03-30 preprint). It takes the same phenomenon Epoch's price-trends dataset documents — prices falling fast and unequally — and adds three things Epoch does not: tier-specific decline rates, a dated structural break in the cause of the decline, and a growth-accounting decomposition of what actually drives cost reduction. Built on integrated datasets (OpenRouter 318 models, Epoch AI 3,237 models, 62 validated milestones 2020–2026).
Key findings
Price decline is tiered (measured):
- Economy tier: ~1.10-year price half-life
- Mid tier: ~1.55-year half-life
- Flagship tier: near-zero exponential fit (R² = 0.031) — flagship prices don't fall cleanly because reasoning models carry a ~31.5x price premium over non-reasoning, scrambling the trend
- Aggregate: ~600-fold price decline (relative; no absolute $/M-with-date in the abstract)
Cause of the decline shifted (estimated):
- Chow test dates a structural break to May 2024 (F = 5.74, p = 0.005): before, decline was technology-driven; after, competition-driven.
- Malmquist productivity index peaked at 4.11 in 2024 Q1–Q4; technological-frontier shift (TC = 4.13) dominates.
- Market concentration fell: HHI 4,558 → 2,086 over three years (moderately concentrated → competitive).
What drives cost reduction (estimated, and the headline claim):
- Total-factor-productivity residuals account for ~103.7% of cost reduction; GPU hardware contributes ~-0.9%.
- I.e. software / architectural innovation, not hardware, drives the price collapse.
- A US–China ~63-fold training-cost gap is attributed to architectural innovation, not factor-price differences (p = 0.228).
Why it matters here
- Sharpens the price-decline-distribution concept — Epoch's 9x–900x spread is here resolved into clean tier half-lives (economy 1.10yr, mid 1.55yr) plus a flagship tier that resists fitting because reasoning premiums dominate. This is a more structured version of the "slow tails are the moat signal" reading already in the KB.
- Direct conflict with the hardware/memory cost story — the ~103.7%-TFP / -0.9%-hardware decomposition says software efficiency, not silicon, is where cost reduction comes from. That cuts hard against Matsuoka's memory-scarcity-as-destiny framing and Patel's memory-shortage thesis: if hardware contributes ~-0.9% to the price decline, a memory crunch may throttle supply without ever having been the source of falling prices. Keep both on the record.
- Competition-driven-since-May-2024 — supports Evans' "compresses toward marginal cost as competition bites" pole over the "rotating-landlord oligopoly" pole, and puts a date on when the mechanism changed.
Limitations
Single-author preprint, abstract-page read only (26pp PDF unread — the ">100% TFP" residual and the Malmquist internals need the full text to judge). Relative price metrics only; no absolute dated $/M-token levels to cross-check against the Token Price Index. Institution not stated on the abstract page.
Source: Tiered Super-Moore's Law by Mingdeng Du, arXiv 2603.28576, 2026-03-30