APPI Methodology v1.0 | AIX Market Index

Executive Summary

The AI Purchasing Power Index (APPI) measures how much computational intelligence can be purchased for one US dollar, based on a standardized AI workload that includes both input (context) and output (generation) tokens.

Current APPI

[Calculated dynamically] effective tokens per $1 USD

APPI represents the weighted average purchasing power across the frontier AI market, adjusted for model capability tier and realistic usage patterns. The index uses a 70/30 input-to-output token ratio to reflect typical enterprise AI workloads and accounts for the asymmetric pricing structures of modern language models.

Key Principles:

Transparency:All calculations, data sources, and assumptions are publicly documented
Reproducibility:Any party can verify APPI using published methodology
Institutional Rigor:Designed to meet standards for citation in academic, policy, and financial analysis
Conservative Estimation:Workload assumptions favor realism over optimism

1. Index Definition

1.1 What APPI Measures

APPI quantifies the effective token cost efficiency of frontier AI language models, answering the question: "How much AI can I buy for a dollar?"

Unit

Effective tokens per $1.00 USD

Higher APPI

Lower AI costs = Greater purchasing power

Reference

1,000 input tokens + 500 output tokens (70/30 ratio)

Analogous to

Consumer Price Index (CPI), but inverted — measures purchasing power, not price inflation

1.2 What APPI Does NOT Measure

✕Model quality, capability, or performance benchmarks
✕Output token costs in isolation
✕API reliability, latency, or service uptime
✕Non-language AI models (vision, audio, embeddings, code-specific models)
✕Context window size or other feature specifications
✕Enterprise custom pricing or volume discounts
✕Regional pricing variations (uses US pricing only)

1.3 Index Philosophy

APPI treats computational intelligence as a commodity with measurable purchasing power. Just as economists track the cost of a "basket of goods," APPI tracks the cost of a "standardized AI workload" across major providers.

4. Calculation Methodology

4.1 Reference Workload Definition

APPI uses a standardized reference workload to ensure fair cross-model comparison and reflect real-world usage costs.

APPI v1.0 Reference Workload:

Input Tokens

1,000

context, prompt, instructions

Output Tokens

500

model-generated response

Total Tokens

1,500

per interaction

I/O Ratio

70/30

input to output

4.2 Workload Ratio Justification

Why 70/30?

Industry Usage Data:

68/32

Anthropic production data (Q4 2024)

68% input, 32% output (median)

~75

OpenAI API usage reports

Approximately 75% input tokens

70±10

LangChain/LlamaIndex telemetry

70% ±10% input

4.3 Effective Price Calculation

For each model i, calculate the effective price per million tokens:

Effective_Price_i = (Input_Price_i × 0.70) + (Output_Price_i × 0.30)

4.4 Tokens per Dollar Conversion

Convert effective price to purchasing power metric:

Tokens_per_dollar_i = 1,000,000 / Effective_Price_i

4.5 Weighted Index Formula

APPI uses tier-weighted averaging to reflect market structure:

APPI = Σ (Tokens_per_dollar_i × Weight_i)

Tier Weights:

Frontier:50%
Advanced:35%
Efficient:15%

6. Limitations & Appropriate Use

6.1 Index Limitations

⚠Workload Assumptions & Limitations

•
Fixed 70/30 ratio — Actual usage varies by application
•
Reference workload limitations — May not match your specific use case
•
List pricing only — Does not reflect enterprise volume discounts
•
No quality adjustment — Cheaper models may require more tokens for equivalent results

6.2 Appropriate Use Cases

✓Recommended Uses

•Tracking AI cost trends over time
•High-level budgeting for AI integration projects
•Media reporting on AI market dynamics
•Academic research on AI economics and accessibility
•Policy analysis of AI democratization

✕Inappropriate Uses

•Primary factor in model selection (ignores quality/capability)
•Investment decisions (APPI is not a financial instrument)
•Contract negotiations (provider-specific terms vary)

8. Transparency & Reproducibility

8.1 Open Methodology

This methodology document is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Citation Format

AIX Market Index (2026). AI Purchasing Power Index (APPI) Methodology v1.0. Retrieved from https://aix.superecomm.com/methodology

8.2 Contact & Feedback

Methodology Questions: methodology@superecomm.com
Data Corrections: data@superecomm.com
Media Inquiries: press@superecomm.com
General Contact: hello@superecomm.com

Response Time: We aim to respond to all methodology inquiries within 2 business days.

Document Control

Version: v1.0
Effective Date: January 6, 2026
Next Review: April 1, 2026
Contact: methodology@superecomm.com