npbuilds Scenario Planner — The Pathfinder
Maps multiple possible futures and tests which paths lead somewhere useful. Not predicting — preparing. The difference matters: prediction says "this will happen." Scenario planning says "here are 3-5 things that could happen — let's make sure our plan works in most of them."
Think of it like packing for a trip where you don't know the weather. You don't predict sun or rain — you pack for both and note which items work regardless.
Core Function
Build structured scenarios for how the AI landscape and skill library could evolve, then evaluate strategies against those scenarios. Every scenario exercise produces:
- A scenario set — 3-5 plausible, distinct futures (not just optimistic/pessimistic)
- Strategy stress-test — How does the current plan perform in each scenario?
- Robust moves — Actions that work well across most/all scenarios
- Contingent moves — Actions that only make sense if a specific scenario materializes
- Signposts — Observable indicators that tell you which scenario is actually unfolding
Scenario Construction
Step 1 — Identify Driving Forces
What are the major uncertainties that could shape the future?
| Category | Example Forces |
|---|---|
| AI capability | Will reasoning improve faster than multimodal? Will context windows keep growing? |
| Tool ecosystem | Will MCP become standard? Will agent frameworks converge or fragment? |
| Cost/access | Will frontier models get cheaper? Will open-source close the gap? |
| Regulation | Will AI regulation constrain capabilities? Enable new markets? |
| Usage patterns | Will AI shift from chat to agentic? From individual to team-based? |
Step 2 — Select Key Uncertainties
Pick the 2 most impactful, most uncertain forces. These become the axes of a 2×2 scenario matrix.
Force A: High
│
Scenario 2 │ Scenario 1
(High A, │ (High A,
Low B) │ High B)
│
Force B: Low ─────────┼───────── Force B: High
│
Scenario 3 │ Scenario 4
(Low A, │ (Low A,
Low B) │ High B)
│
Force A: Low
Step 3 — Build Each Scenario
For each quadrant, construct a narrative:
| Element | Description |
|---|---|
| Name | A memorable label (not "Scenario 1" — something evocative) |
| Narrative | 2-3 sentences describing this world |
| Key features | What's true in this future that isn't true today? |
| Library implications | What skills become more/less valuable? What new domains emerge? |
| Probability estimate | Rough likelihood (these should sum to ~100% across the set) |
Step 4 — Stress-Test Strategy
For each scenario, evaluate:
| Question | Assessment |
|---|---|
| Does our current build plan still make sense? | [yes / partially / no] |
| Which planned skills become more valuable? | [list] |
| Which planned skills become less relevant? | [list] |
| What skills would we wish we'd built? | [list] |
| What skills would we regret building? | [list] |
Step 5 — Classify Actions
| Action Type | Definition | Example |
|---|---|---|
| Robust | Works in 4+ of 5 scenarios | Building cross-domain connection skills (valuable regardless of AI direction) |
| Contingent | Works in 1-2 scenarios only | Building a domain that only matters if a specific AI capability materializes |
| Hedging | Costs little now, pays off big in one scenario | Adding a reference file that prepares a skill for a capability that might arrive |
| No-regret | Positive in all scenarios, even if magnitude varies | Improving clarity-engine (explanation is always valuable) |
Step 6 — Identify Signposts
For each scenario, what observable signals would tell you it's the one actually unfolding?
| Scenario | Signpost | Source |
|---|---|---|
| [Name] | [Observable indicator] | [Where to watch for it] |
Scenario Archetypes
Common scenario shapes that recur in AI landscape planning:
The Leap
One capability jumps dramatically (e.g., reasoning goes from "good" to "superhuman"). Everything that depends on that capability suddenly needs redesigning.
- Library impact: Skills that assumed limited capability need urgent upgrades
- Robust response: Build skills with adjustable capability assumptions
The Plateau
A previously fast-improving capability levels off. What everyone assumed would keep getting better... doesn't.
- Library impact: Skills optimized for "future capability X" become over-designed
- Robust response: Don't build skills that require capabilities that don't exist yet
The Convergence
Multiple capabilities improve simultaneously and interact in unexpected ways (e.g., reasoning + tool-use + long context = autonomous agents).
- Library impact: New skill categories emerge at the intersection
- Robust response: Build connective tissue between domains — convergence rewards integration
The Fragmentation
The AI ecosystem splits (e.g., open vs. closed models diverge, regional regulations create different capability landscapes).
- Library impact: Skills may need to account for different capability tiers
- Robust response: Keep skills capability-agnostic where possible
Output Format
SCENARIO PLANNING — [Topic]
Time Horizon: [6 months / 1 year / 2 years]
Key Uncertainties: [Force A] × [Force B]
Scenarios:
1. [Evocative Name] (probability: ~N%)
Narrative: [2-3 sentences]
Library Impact: [what changes for us]
2. [Evocative Name] (probability: ~N%)
...
3-5. ...
Strategy Assessment:
Robust moves (work in most scenarios):
- [action]
Contingent moves (scenario-dependent):
- [action] — only if [scenario name]
No-regret moves:
- [action]
Signposts to watch:
- [indicator] → suggests [scenario name]
Recommendation:
[1-2 sentences: what should we do given this analysis?]
What This Skill Does NOT Do
- Predict — Scenarios are plausible futures, not forecasts. Assigning probabilities is for calibration, not prophecy.
- Decide — Scenario-planner stress-tests strategies. Growth-architect decides what to build.
- Evaluate current state — That's frontier-scanner (for AI) and skill-cartographer (for the library). Scenario-planner takes their data and projects it forward.
Cross-Domain Connections
- Neocortex/foresight/frontier-scanner: Frontier data is the primary input for scenario construction
- Neocortex/architecture/growth-architect: Scenario outputs directly inform build plan robustness
- Neocortex/foresight/briefing-engine: Scenarios are a key component of strategic briefings
- Philosophy/decision-theory/decision-architect: Shared toolkit — both work with uncertainty, options, and outcomes. Decision-architect handles individual decisions; scenario-planner handles strategic futures.
- Philosophy/decision-theory/counterfactual-reasoner: Complementary — counterfactual looks backward ("what if X hadn't happened"), scenario-planner looks forward ("what if X happens")
- Investing/regime-intelligence: Regime shifts in markets parallel paradigm shifts in AI — similar mental model, different domain