Arbitrage Audit

Arbitrage Audit

Org-level diagnostic that identifies which inefficiencies a business is monetizing, how fast AI will compress each one, and what the next upstream gap looks like once compression happens. Based on Nate's "industries rest on exploitable inefficiencies" thesis.

Trigger

Use when the user says "audit my business", "where am I exposed to AI", "arbitrage audit", "what's my AI risk", "how much of my work can AI do", "margin compression", or describes a product/service and asks what happens when the next model ships.

Phase 1: Intake

Collect the following. Ask for anything missing:

1. Business model — one-sentence description of what the company sells and how it charges
2. Core value prop — why customers pay (speed? expertise? access? coordination?)
3. Cost structure — where does time/money actually go? (labor hours, tooling, overhead)
4. Pricing basis — hourly? per-seat? per-outcome? per-project?
5. Current AI usage — is AI already in the workflow, and where?

If the user gives a one-liner, probe for at least items 2, 3, and 4 before continuing. The audit is worthless without real cost structure.

Phase 2: Gap Classification

For each of the five gap categories, determine whether the business monetizes it and rate compression velocity. Output one row per category:

Structural gaps (regulation, physical constraints, trust, relationships) compress slowly or not at all. Informational gaps (any inefficiency made of text, data, or logic) compress at model-release cadence.

Phase 3: Compression Timeline

For every monetized informational gap, project compression on three horizons:

• Now → 6 months — what current frontier models already do adequately
• 6 → 18 months — what the next 1-2 model releases will collapse
• 18+ months — what survives because it is structural

Cite concrete signals: named model capabilities, agent framework releases, vendor launches. Do not speculate beyond public signal.

Phase 4: Action Plan

Output three prioritized actions:

1. Migrate — which monetized gap should the business abandon first, and what upstream gap replaces it?
2. Harden — which structural gap should become the new primary moat?
3. Build — what sensing/systems capability needs to exist so the business can rotate again when the next model ships?

Each action gets: a one-sentence rationale, an estimated cost/effort tier (weekend / multi-sprint / quarter), and a success metric.

Phase 5: Output Format

Produce a single markdown document with sections:

# Arbitrage Audit — {business name}

## Thesis
{one paragraph: where is the business exposed, where does it survive}

## Gap Table
{the Phase 2 table}

## Compression Timeline
{three horizons, concrete signals}

## Action Plan
1. Migrate: ...
2. Harden: ...
3. Build: ...

## Follow-up Questions
{anything that would sharpen the audit if the user supplied more data}

Verification

Before returning the audit, check:

• Every "yes" in the Monetized? column has at least one line of evidence from user input
• Every compression-velocity rating cites a model, vendor, or public signal — no hand-waving
• The migrate action names a concrete upstream gap, not just "move upmarket"
• The build action is actionable inside the user's current team size

If any check fails, ask the user for the missing information before producing the final report.

Source

Derived from Nate's Newsletter 2026-04-07, "You're charging 2023 rates for work AI does in 40 minutes + 2 prompts to see your real exposure." Nate ships this as one of two diagnostic prompts; this skill operationalizes it as a structured audit with explicit classification, timeline, and action output.