Occupant Indices

The premise

Every major input to the economy has a published number you can look up — a spot price for crude, a CPI print for groceries, a wholesale rate for electricity, a capacity factor for a wind farm. Occupant builds the same kind of instrument for AI compute: a standardized, dated, methodology-driven yardstick that anyone — a procurement officer, an investor, a policy researcher, or another model — can read cold and act on.

It’s infrastructure, with the same access for everyone. The model is closer to a public yield curve than a SaaS dashboard.

The indices

$CPICompute Price Indexthe price signal

Measures: the price of AI work at the basket level.

$CPI defines a fixed basket of standardized workloads and re-prices that basket every day across capability tiers, volume-weighted over 2,188 models and normalized to February 2025 = 100. It’s a deflator: hold the basket constant, watch the cost move.

• Capability tiers: $BULK (commodity models under $1/MTok), $FRONT (frontier capability, ranked by Arena ELO), $JUDGE (reasoning-intensive, o1 / o3 / R1-class), $LCTX (long-context, 128K+).
• Build Cost sub-indices apply realistic, fixed workload mixes so a reader lands on the number that matches their own usage: $START (startup builder), $AGENT (agentic systems), $THRU (high-throughput).
• Published across multiple horizons — since-launch, year-over-year, quarter- and month-to-date, week-over-week.

Reading it: at ~62.4, a basket of AI work costs about 38% less than at launch, and fell ~9.7% in the most recent month.

$AIUAI Economic Activity Indexthe volume signal

Measures: the volume of AI economic activity, expressed as a synthetic unit (the AIU) modeled on the IMF’s Special Drawing Rights.

The AIU is a weighted composite:

• 60% token throughput — usage intensity.
• 30% inferred spend — tokens × blended pricing, the economic scale.
• 10% energy proxy — a blend of token-derived energy estimate (70%) and global grid-investment growth (30%), so the energy term carries independent information.

Baseline February 2025 = 100. At ~503.65 it reflects roughly a 5× expansion in activity over about fifteen months — on the order of 42.7 trillion tokens and $210M of inferred spend per week.

Reading them together: total AI spend ≈ AIU × CPI. This decomposes the headline spending figure into its two drivers — efficiency gains (price) and demand growth (volume) — which is exactly the split the “is this a bubble?” debate needs.

$LDILabor Displacement Indexthe labor signal

Measures: the pressure AI puts on real jobs, reported as two distinct numbers that answer two distinct questions. The gap between them is the finding.

• Cost differential (structural). What AI could displace at today’s pricing. Across nine pilot federal workloads, average human cost runs ~$53.56/unit against ~$0.0062/unit for AI — a cost ratio on the order of 37,000× and a structural displacement potential near $4.2B/year. A pressure gauge.
• Substitution rate (observable). What procurement records show actually shifting — ~3.86%.
• Absorption dimension. When a workload automates, the index classifies whether the workforce is cut, frozen, or reallocated — because “cheaper” and “fired” are separate events.

Built on named public data: BLS OEWS + ECEC wages for human cost, FPDS / USAspending contractor spend as substitution proxy, OPM FedScope FTE counts for headcount, mapped through SOC and PSC crosswalks.

Worked end-to-end: the SNAP Eligibility deep dive resolves all three pillars to one real program — ~$25.13 per human review vs ~$0.000256 for AI, ~41M reviews/year, a ~2.1% substitution signal, and an “absorption: reallocated” verdict as caseload grows against a flat workforce. Every step is auditable on the page.

The tools

The indices are the readings; the tools let people act on them.

• LLM Cost Calculator. Enter a monthly spend or raw token volumes, pick a workload profile, and compare a tiered model mix at live $CPI rates. Answers “lock in now or wait a quarter?”
• LDI Workload Calculator. Run the human-vs-AI cost and substitution math on a workload of your own.
• AI Services Price Reasonableness Worksheet. A print-ready, FAR-style determination sheet a contracting officer can fill in, sign, and attach to an RFP, built on the $CPI basket.
• Market Intelligence. “Sabermetrics for models” — Quality-Adjusted Price, cognitive arbitrage (≈90% of frontier capability at ≈40% of the cost), and tier analysis across the market.

How it’s built

The architecture follows directly from the infrastructure-first mandate.

Frontend

Vanilla HTML5 / CSS3 / JavaScript. Flat files on any static host.

Data pipeline

Python 3 in src/: dedicated fetchers per source (OpenRouter, LiteLLM, BLS, FPDS / USAspending, OPM FedScope, BloombergNEF grid data), calculators (calculate_cpi, calculate_aeai, calculate_ldi), a model-tier registry, signal derivation, and historical backfill.

Storage

Flat-file JSON in data/, including machine-readable endpoints another agent can pull as a grounded, dated source.

Automation

GitHub Actions on a daily cron recompute the indices and commit fresh JSON. The git history is the data lineage — every published number is reproducible from the commit log.

Algorithms

Deterministic, documented formulas with published weights and baselines.

Privacy

Client-side localStorage for the theme toggle only.

License

Parachute Commons: free for personal, educational, and non-commercial use; tiered terms at commercial scale.

Cost to operate

~$50/month.

What this piece demonstrates

• Economic index design. Baskets, volume weighting, normalization, base periods, and a synthetic composite unit (the AIU’s SDR analogy), applied to a new domain.
• Data engineering on messy public sources. Reconciling price discrepancies across OpenRouter and LiteLLM, building SOC ↔ PSC ↔ FedScope crosswalks, and designing fallback hierarchies.
• Domain range, coherently connected. CPI / macro methodology, federal procurement (FAR, FPDS, FedRAMP), labor economics (BLS, JOLTS, OPM), and energy capital expenditure.
• Product and communication judgment. Designing for a thirty-second read by a procurement officer and holding a deliberate editorial voice.
• Pragmatic systems architecture. Static-site-plus-cron with git as an audit log; three indices that compose, where the LDI consumes the live CPI basket.

Occupant is the price tag, the activity meter, and the labor gauge for AI — public, daily, and auditable — because a market this large deserves a shared number.

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