Inversion

Inversion

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Overview

Most planning asks "how do I win?" and runs forward from there. Inversion runs the other way: "how could this fail catastrophically?" — then designs the plan around eliminating the failure paths that matter most. The work is not pessimism; it is eliminating known ways to lose so you keep only the risks you can live with.

This is one of four composable motions in the deciqAI collection: first-principles decomposes downward to bedrock; occams-razor chooses sideways among competing accounts; second-order-thinking traces forward through time; inversion traces backward from failure. Compose freely — use inversion after a first-principles teardown, alongside a parsimony audit, or in parallel with a forward cascade as a failure cascade.

When to Use

Apply when: decision is high-stakes or hard-to-reverse; enthusiasm is high but no risks named; someone says "pre-mortem," "what could go wrong," "why might this fail," "how could our AI launch fail," or is riding AI hype into a shipping decision with no failure modes named.

When NOT to use: reversible low-stakes calls; immediate crisis requiring action now; lack domain knowledge to enumerate plausible paths.

Coaching Novices (Adaptive Front Door)

Two delivery modes: Engine mode — user has a concrete decision → run the full Audit directly. Coach mode — user signals unfamiliarity → guide one step at a time. When unsure: "Want me to run this on a specific decision, or walk you through the method?"

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output that step's question and nothing more.

1. One-line what-it-is. Flips "how do I win?" to "how could this fail catastrophically?" — eliminates load-bearing failure paths up front so you commit with eyes open.
2. Check fit. Match against When to Use / When NOT to use. If it doesn't fit, say so and point elsewhere.
3. Elicit their real decision. If no concrete case, ask for one. Never invert a hypothetical when a real decision is available.

[WAIT — do not advance until user responds]

1. One step at a time. Force them to name 5–7 failure paths in their own words before classifying; rank together; design mitigations one at a time.

[WAIT — do not advance until user responds]

1. Close by naming the payoff. Name the one failure path they had not seen — the new entry on their not-to-do list.

[WAIT — do not advance until user responds]

The Process

Run the Inversion Audit. Trace backward from failure, rank by load-bearing, then mitigate.

1. State decision + measurable target outcome ("12-month MAU ≥ 100K," not "the product succeeds").
2. Invert: "If this is a total failure by , the most likely reasons are ___." Give explicit permission to speak badly of the plan.
3. Enumerate failure paths (aim 5–10), uncharitably. Do not filter. The paths you don't write down quietly survive.
4. Classify and weight each: P(occur) × Impact + category tag: Internal / External / Assumption failure / Timing.
5. Design a response for every load-bearing path (high-P × high-Impact): ELIMINATE / MITIGATE / HEDGE / ACCEPT-with-plan.
6. Build your Not-to-Do list. Crystallize "even under pressure, we will not do X" rules from the load-bearing failure paths.
7. Name the abort trigger: "We commit, and we will abort if ___ happens by ___ date."

Output: the Inversion Audit

# Inversion Audit: 
## Target outcome (measurable): 
## Inversion question: "If this is a total failure by , the most likely reasons are..."
## Failure paths (uncharitable): 1.  — category: internal/external/assumption/timing — P: × Impact:
## Load-bearing paths:  → ELIMINATE / MITIGATE / HEDGE / ACCEPT-with-plan — 
## Not-to-Do additions: 
## Abort trigger: "We will abort and re-invert if  happens by ."
## What I might still be missing: 

→ Method in Action: Apollo 1 and the FMEA Mandate (1967) · Wald's Bomber Survivorship Analysis (1942–1943) → 2026 lens: Inverting an AI Product Launch (2024–2026)

Inversion Packs

The Inversion Audit runs the same way everywhere, but the failure-mode catalog differs by domain. In startup fundraising: dilution at terms that make later rounds unraisable; misreading what the next round requires; taking strategic money that closes off other strategics. In software launches: capacity failure at launch; onboarding drop-off; negative review cascade; platform-policy surprises.

Adding an inversion pack for your domain is the easiest way to contribute — one self-contained file. See the contribution template at the repo root.

Applying It Well

• Deliverable = mitigations, not a scary list. Failure list without paired responses is anxiety in spreadsheet form.
• Specificity beats coverage. Three concrete mechanisms beat ten vague paths.
• Anonymity unlocks honesty. Written independent submissions surface the failure paths people actually fear (Klein 2007).
• The Not-to-Do list compounds. Audits expire; the rules they generate persist.
• Startups & investing: eliminate catastrophic-loss paths first; merely-good paths take care of themselves.

→ Sources: references/sources.md

Common Rationalizations

The ways people fake inversion. If you catch yourself in the left column, you are running a critique that doesn't change behavior.

Note — [D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• Generic placeholders in failure-path list ("execution," "market") with no specific mechanisms
• No new information surfaced — every path was already discussed
• No P × Impact ranking; no mitigation or abort trigger paired with any path
• Senior person dominated; dissenting paths were socially suppressed
• Audit < 30 minutes; "we did a pre-mortem" used as proof of rigor with nothing in the plan changing

Verification

• Target outcome is measurable (number + timeframe)
• ≥ 5 failure paths enumerated uncharitably; each names a specific mechanism, not a placeholder
• Each path carries P(occur) × Impact rating and a category tag
• Every load-bearing path has a paired response: ELIMINATE / MITIGATE / HEDGE / ACCEPT-with-plan
• Explicit abort trigger named; ≥ 1 entry added to the Not-to-Do list

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Part of deciqAI Knowledge Skills — 233 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/inversion · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/inversion.json