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QP-019 • PF-01 Template / Workbook

Compare AI Cost, Energy, and Resource Use

Official Q product: EE Total Cost and Energy Workbook - Public Artifact Edition

This is a workbook that helps you see the money, energy, computing, and other resource costs of an AI or technology choice before committing to it.

Good for: AI teams, infrastructure teams, buyers, planners, and decision-makers comparing technology choices
Level 1 · Discover · Free

What is this, and why might you need it?

Start here. No purchase is required to understand the product.

What it is

This is a workbook that helps you see the money, energy, computing, and other resource costs of an AI or technology choice before committing to it.

What problem it helps with

Compare the full cost and resource burden of a technology choice instead of looking only at performance or purchase price.

Who it is for

AI teams, infrastructure teams, buyers, planners, and decision-makers comparing technology choices

Important limits

No savings percentage without measurement
Level 2 · Learn & Evaluate · Free

Study it before you buy it.

This is the pre-purchase learning and decision layer. It stays public even when the complete package is not yet ready to order.

Two-minute study

QP-019 — EE Total Cost and Energy Workbook - Public Artifact Edition
TWO-MINUTE RESEARCH PAPER · 1.0.0-r1 · 2026-09-25
STATUS: CANDIDATE RESEARCH — HUMAN/INDEPENDENT REVIEW REQUIRED

WHAT IT IS
This is a workbook that helps you see the money, energy, computing, and other resource costs of an AI or technology choice before committing to it.

PROBLEM
Compare the full cost and resource burden of a technology choice instead of looking only at performance or purchase price.

WHO IT IS FOR
AI teams, infrastructure teams, buyers, planners, and decision-makers comparing technology choices

WHY IT MATTERS
AI-enabled work can fail through weak evidence, stale information, unclear authority, privacy/security gaps, overconfident outputs, or automation that becomes practically irreversible. NIST's AI RMF provides a lifecycle risk-management frame; Q10 applies that direction by requiring explicit scope, evidence, human authority, limitations, review dates, stop conditions, and rollback.

HOW IT WORKS
Name a human owner; define scope; record evidence, unknowns and contradictions; define permissions and prohibited actions; run baseline and adversarial tests; patch and retest; record residual risk; create a dated release receipt; set a review/TTL date.

VALIDATION STATE
Metered workload calibration
Generated research does not upgrade that evidence.

RISKS / FAILURE MODES
Scope creep; evidence drift; stale sources; authority escalation; false-clear from passing tests; privacy leakage; insecure dependencies; common-mode failure; reviewer fatigue; inaccessible interfaces; rollback failure; unsupported claims repeated as fact.

HUMAN CONTROL
A named human retains consequential approval, override, stop, release, and rollback authority.

KNOWN LIMITS
No savings percentage without measurement

REFERENCE BASIS
NIST AI Risk Management Framework 1.0; NIST Generative AI Profile; NIST Privacy Framework; NIST Cybersecurity Framework 2.0; OWASP Top 10 for LLM and GenAI; UNICEF Guidance on AI and Children v3.

BOUNDARY
Research/education material only. Not legal, medical, financial, engineering, safety-certification, or regulatory advice.
Open buyer / research paper

How it works

A separate operating explanation has not yet been recorded for this product. Review the buyer/research paper and technical details below for the currently documented implementation information.

AI boundaries & human responsibility

AI-specific allowed/not-allowed actions have not yet been separately recorded for this listing.

Risks, limits & failure modes

No savings percentage without measurement

Failure modes: no separate failure-mode record has been published for this listing yet.

Dependencies, compatibility & deployment

Dependencies: not separately recorded.

Evidence, testing & provenance

Metered workload calibration

Provenance:
Factory source class: CANONICAL_82
Q spine/source basis: EE; T2Y; TTL
Factory source: shopify:ee-total-cost-and-energy-workbook

Independent validation: No independent-validation status has been recorded; no independent validation is claimed by this listing.

Resources, privacy & security

Separate resource/energy requirements are not yet recorded on this listing.

Privacy and security obligations depend on the product and deployment. Where they have not yet been versioned in the legal/disclosure manifest, they remain an open pre-sale requirement rather than an implied promise.

What the complete package is expected to include

Editable cost and burden model

Price, license, support & updates

Price / pricing method: Request access • estimate after scope if services are needed

License: Not yet recorded as a versioned license model.

Legal, rights & disclosures

A plain-language legal summary has not yet been versioned for this product.

Legal manifest status: LEGAL_SNAPSHOT_NOT_YET_VERSIONED

Level 3 · Complete Q Package

The order must deliver the complete promised package.

Level 3 is fulfillment, not more marketing. An email, request form, quote request, or buyer paper is not the purchased package.

What this version promises

Editable cost and burden model

Recorded package filename: QP-019_ee-total-cost-and-energy-workbook_v1.0.0.zip

Product version: 1.0.0

Order readiness

NOT READY TO ORDER YET

This listing stays open for Discover and Learn & Evaluate, but Q will not treat Level 3 as sale-ready until the complete-package proof is finished.

Package contents + version
Recorded
Fulfillment manifest
PACKAGE_LOCATED__CONTENTS_VERIFICATION_REQUIRED · manifest file missing
Legal/disclosure manifest
LEGAL_SNAPSHOT_NOT_YET_VERSIONED · legal snapshot file missing
Delivery path
PROVIDER_NOT_BOUND
Checkout release
HELD_UNTIL_PRODUCT_SPECIFIC_GATE_CLEARS
Release gate
Not cleared

Planning or configuring is not an order and does not count as fulfillment.

THE Q PATH

Discover → Learn & Evaluate → Complete Package.

Stop after learning, save it to My Q, combine it with other Q products, or order the complete implementation only when its delivery gate is actually ready.