Project 30: Strategic Timing, Platform Risk, and Moat Simulator
Quantify strategic risk and moat options for an agent business over a 24-month horizon.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Level 4: Expert |
| Time Estimate | 10-18 hours |
| Language | SQL + Markdown (alt: Python, TypeScript) |
| Prerequisites | Projects 21, 26, 28 |
| Key Topics | market timing, provider dependency, API volatility, moat strategy |
Learning Objectives
- Model strategic risks with quantified impact and probability.
- Simulate provider/API shocks and resulting margin pressure.
- Score moat options across data, workflow, distribution, and integration.
- Produce strategy recommendations tied to measurable signals.
The Core Question You’re Answering
“How do you build an agent business that remains defensible under platform and market volatility?”
Concepts You Must Understand First
| Concept | Why It Matters | Where to Learn |
|---|---|---|
| Platform dependency | concentration risk can break roadmap | platform strategy references |
| Commoditization curves | features lose differentiation quickly | market economics studies |
| Moat taxonomy | guides durable investment choices | strategy literature |
| Scenario planning | prepares for adverse futures | risk planning methods |
Theoretical Foundation
Market Signals + Provider Risk + Cost Curves + Moat Score -> Strategic Priorities
Strategy quality comes from explicit tradeoffs, not optimistic narratives.
Project Specification
What You’ll Build
A strategy simulator that outputs:
- Risk register (timing, provider, API, commoditization)
- Scenario outcomes (base/shock/downside)
- Moat scoring matrix
- Recommended roadmap priorities
Functional Requirements
- Risk scoring model
- Scenario simulation engine
- Moat scoring rubric
- Board-ready strategy memo output
Non-Functional Requirements
- Transparent assumptions
- Sensitivity analysis support
- Clear actionability for roadmap decisions
Real World Outcome
$ python p30_strategy_sim.py --horizon 24m
[timing] window=active confidence=0.74
[platform_risk] concentration=0.81 volatility=0.67
[commoditization] feature_half_life=8_months
[moat_scores] workflow=0.84 integration=0.79 data=0.72 distribution=0.61
[artifact] strategy_memo_q2.md
Architecture Overview
Signal Ingest -> Risk Engine -> Scenario Runner -> Moat Scorer -> Strategy Writer
Implementation Guide
Phase 1: Risk Inputs
- Define metrics and thresholds for each strategic risk.
Phase 2: Scenario Simulation
- Run base, shock, and downside cases.
Phase 3: Moat Prioritization
- Rank investments by retention and margin impact.
Testing Strategy
- Assumption sensitivity tests
- Stress tests under doubled model cost
- Provider outage scenario tests
Common Pitfalls & Debugging
| Pitfall | Symptom | Fix |
|---|---|---|
| Narrative-only strategy | weak decisions | force quantified risk scoring |
| Single-provider assumptions | fragile roadmap | simulate provider shocks |
| Feature moat illusion | fast copycat pressure | score workflow/integration depth |
Interview Questions They’ll Ask
- How do you quantify platform dependency risk?
- What signals indicate commoditization pressure?
- Which moat investments compound best over time?
- How do you convert risk analysis into roadmap priorities?
Hints in Layers
- Hint 1: Keep risk metrics few and explicit.
- Hint 2: Model adverse scenarios before base case optimism.
- Hint 3: Tie moat score to retention and switching cost.
- Hint 4: Publish assumptions with confidence labels.
Submission / Completion Criteria
Minimum Completion
- Strategic risk register + one scenario run
Full Completion
- Three scenarios + moat matrix + strategy memo
Excellence
- Evidence-backed roadmap and mitigation plan approved by leadership