Inference & Token-Pricing Economics

Quantitative 5-scenario model (Rotating Landlord Oligopoly 25% / Commoditization Crash 25% / Jevons Absorption 20% / System-Layer Re-differentiation 18% / Geopolitical Bifurcation 12%) for how DRAM/HBM pricing, open-weight frontier models, and compute-resale entrants (Meta, xAI) restructure AI-industry economics 2026-2030; introduces the 'depreciation conveyor' mechanic explaining why incumbent fleets stay cost-advantaged even as hardware prices normalize. Directly engages this topic's open 'commodity infrastructure?' question.

Shows identical H100 hardware yields $0.21-$15.25 per million output tokens (2.5x-36.3x variation) purely from request-rate/utilization assumptions — utilization-as-fixed-parameter is the dominant error source in cost calculators. Introduces C_eff = f(H,M,Q,λ,L) framework + open-sourced vllm-cost-meter tool, validated on 42 benchmarks. Directly relevant to grounding any 'cost per token' claim cited in the Evans/Patel margin debate this topic's frontier.md already tracks.

First attempt to model AI inference cost as a first-order marginal-cost input to the macroeconomy: an augmented New Keynesian 'Inference-Cost Phillips Curve' (ICPC), estimated via GMM against 2022-2026 US + G7 panel data (slope coefficient 0.087, near-unit-elasticity pass-through). Speculative but a genuinely novel lens for where token-pricing economics could matter beyond the AI-lab P&L question this topic otherwise tracks.

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.

Formalizes cost-per-TASK: 'cost-of-pass' = expected monetary cost of a CORRECT solution (per-token cost / accuracy); 'frontier cost-of-pass' = min across models or human experts. Frontier cost-of-pass for complex quantitative tasks 'roughly halved every few months'; model-level (not inference-time) innovation drives gains. The canonical framework for the unit this topic said actually matters. Original 2025-04-17, revised 2026-02-26.

Proposes 'AI tokenomics' as a formal field; load-bearing claim: token EXPENDITURE and economic VALUE are distinct — value depends on marginal productivity, workflow position, hidden reasoning activity, risk, downstream propagation, NOT tokens burned. So per-token pricing prices the input not the output. Names 'hidden-token measurement' and 'empirical calibration' as open — conceding the theory has no measured cost series.

State-based personal-agent benchmark (40 tasks, 11 frontier models) scored by final SYSTEM-STATE correctness rather than text; reports a measured per-task API cost ~$0.17 (cheapest, MiniMax-tier) to ~$6.55 (Claude-Opus-4.7-tier) — a ~38x spread — the KB's FIRST dated absolute cost-per-task levels, partly closing the topic's sharpest cost-per-task gap. The $0.17-$6.55 figures are search-surfaced from the paper's cost table, NOT in the fetched abstract; full-text confirmation owed. API list-price-based, personal-computing scenarios only.

Two-year re-run of the WorkBench workplace-agent benchmark: best-agent task completion 43% (GPT-4, Mar-2024) -> 98% (Claude Fable 5, mid-2026); unintended harmful actions 26% -> 1.9% (capability and safety moved TOGETHER). KB-relevant finding: open-weight models collapsed the cost of a given performance level while FRONTIER serving costs stayed flat — a direct benchmark datapoint that the price decline shows up in the open-weight rung, not the frontier rung (open-weight-driven, ~100x since 2024 per the body). Per-tier $ tables not in abstract; full-text read owed.

Treats the token as a physical quantity with a measurable thermodynamic cost (Landauer's principle + Shannon channel capacity) and builds a supply/demand balance sheet for global token production — deriving the energy->token bridge the KB's 'no energy pass-through leg' gap was missing: the projected 2028 US AI energy allocation of 326 TWh could support ~6.5x10^17 tokens/yr ~225,000 tokens/person/day, >3 orders of magnitude above mid-2024 utilization (i.e. energy is not the near-term binding constraint on token VOLUME; direction — which questions are worth asking — is). Order-of-magnitude policy-framing estimates, not a measured per-SKU cost series.