m2ai-portfolio AI Cost-Structure Exposure Audit
A three-prompt sequential audit that maps an organization's exposure to the AI inference cost curve -- who owns the inference layers you depend on, what happens when subsidized pricing converges to real unit economics, and which workloads can move on-device vs which are cloud-bound.
Trigger
Use when the user says "/ai-cost-exposure-audit", "audit my AI cost exposure", "map my inference dependencies", "what's my exposure to AI pricing changes", "cost curve audit", or "find my AI exposure".
Phase 1: Intake
Collect (or infer from context):
- Organization type -- startup / SMB / enterprise / individual practitioner
- Primary AI use cases -- list of workflows that depend on LLM APIs (e.g., customer chat, code generation, document processing)
- Providers in use -- OpenAI, Anthropic, Google, Azure OpenAI, OpenRouter, self-hosted, etc.
If given a codebase path, offer to scan it: grep for API client imports and model name literals to auto-populate the provider list.
Tell the user:
Starting AI Cost-Structure Exposure Audit
Organization: {type}
Providers detected: {list}
Working through 3 audit prompts...
Phase 2: The Three-Prompt Audit
Run these prompts sequentially -- each builds on the prior output.
Prompt 1 -- Dependency Inventory
Present to the user:
"List every workflow in your organization where an AI inference call is required to complete the task (not just 'nice to have'). For each: which provider, which model tier (small/medium/large/frontier), and is the model name hardcoded or configurable?"
Wait for the user's response, then produce a structured table:
| Workflow | Provider | Model Tier | Hardcoded? | Notes |
|---|
Flag any workflow where the model is hardcoded to a specific frontier model (e.g., gpt-4o, claude-opus-4-7) -- these carry the highest lock-in risk.
Prompt 2 -- Sensitivity Analysis
Using the dependency table from Prompt 1, present:
"For each workflow above: what would happen to your unit economics if that provider's API price doubled tomorrow? Which workflows would you cut, which would you absorb, and which would break your margin?"
Produce a sensitivity matrix:
| Workflow | Current Cost Tier | 2x Price Impact | Response |
|---|---|---|---|
| ... | Low / Med / High | Tolerable / Margin Pressure / Business-Breaking | Cut / Absorb / Must-find-alternative |
Highlight any workflow marked Business-Breaking -- these are the exposure points that require mitigation before the inflection happens, not after.
Prompt 3 -- Portability Assessment
For each high-risk workflow surfaced in Prompt 2, present:
"For each business-breaking workflow: could it run on a smaller model (e.g., 8B-70B local) with acceptable quality loss? Is the output latency-sensitive? Does the data involved allow cloud processing, or is there a regulatory/privacy constraint forcing local inference?"
Produce a portability table:
| Workflow | Smaller Model Viable? | Latency Constraint | Data Sensitivity | Can Go On-Device? |
|---|---|---|---|---|
| ... | Yes / No / Partial | Real-time / Batch | Public / Internal / Regulated | Yes / No / Partial |
Workflows where Can Go On-Device? = No and 2x Impact = Business-Breaking are the critical exposure points -- cloud-bound and economically fragile.
Phase 3: Exposure Summary
Produce a one-page summary:
=================================================================
AI COST-STRUCTURE EXPOSURE AUDIT
=================================================================
Date: {date}
Organization: {type}
CRITICAL EXPOSURE (cloud-bound + business-breaking at 2x price):
{list workflows}
MANAGEABLE EXPOSURE (absorb or cut at 2x):
{list workflows}
ON-DEVICE CANDIDATES (portability score: High):
{list workflows}
TOP RECOMMENDATIONS
1. {highest-leverage action -- e.g., replace hardcoded model with configurable env var}
2. {next action}
3. {next action}
NOTE: This audit reflects stated dependencies, not a live cost analysis.
Verify current pricing via each provider's pricing page before
acting on sensitivity estimates.
=================================================================
What This Does NOT Do
- Does not calculate exact dollar costs -- pull your usage dashboard and multiply by the rate change you are stress-testing.
- Does not recommend specific hardware for on-device migration -- scope that separately from the portability candidates.
- Does not scan codebases automatically unless the user consents to the scan in Phase 1.
Source
Derived from Nate Kadlac newsletter (2026-04-26): "Executive Briefing: The AI cost curve your strategy is riding just broke + 3 prompts to find your exposure." Public teaser only -- the specific prompts are paywalled. This skill reconstructs the three-prompt shape from the section headers and thesis.