| name | product-strategy |
| description | Structured product strategy framework for AI product roles. Covers: market entry, competitive positioning, build-vs-buy, long-term vision. |
| argument-hint | [interview question] |
Product Strategy Skill
Apply a structured framework to PM product strategy questions targeting AI product roles.
When to Use
- User asks "What strategy would you use for X"
- User asks "How would you enter market X"
- User asks "Define the product strategy for X"
- User asks "How would you decide between X and Y" (strategic choice)
- User asks about competitive positioning, market entry, or long-term vision
- User says
/product-strategy followed by a question
- Any question about go-to-market, competitive dynamics, build-vs-buy, or strategic direction
Context
- Tuned for: AI product roles at frontier AI companies
- What matters: Zooming out to the 30,000-foot view. These companies operate at the frontier — the best strategic thinking reasons about where the market is going, not just where it is.
- Common pitfall: Not landing a clear position. You must identify where the moat is, where commoditization is happening, and connect mission to business goals.
Framework: Product Strategy (5 Sections)
Section 1: Strategic Alignment & Clarifications
Ask 3-5 clarifying questions:
- Scope: Which product line? What timeframe (6mo vs 3yr vs 10yr)?
- Constraints: Are we resource-constrained? What's the competitive urgency?
- Success: What does winning look like? Revenue? Market share? Mission impact?
- Context: Any recent market shifts or company announcements to consider?
State the strategic question clearly in one sentence. Then:
- Company Mission: Restate and connect to the question
- Current Position: Where does the company stand today on this?
- Strategic Tension: What's the core trade-off or decision at the heart of this question?
Section 2: The Landscape (Market & Leverage)
Market Analysis:
- Market size (TAM/SAM/SOM) with reasoning
- Growth rate and trajectory
- Key trends reshaping the landscape (AI adoption, regulation, platform shifts)
Competitive Map:
- Direct competitors and their positioning
- Indirect competitors and substitutes
- Where is the market commoditizing? Where is there differentiation?
Porter's Five Forces (applied to AI context):
- Threat of new entrants (open-source models, startups)
- Supplier power (compute providers, data sources, talent)
- Buyer power (enterprise vs consumer, switching costs)
- Threat of substitutes (alternative approaches, non-AI solutions)
- Competitive rivalry (between major AI labs and open-source)
Unique Leverage: What does THIS company have that others don't?
- For a model provider with distribution advantage: Consumer product reach, model capability leadership, developer ecosystem, strategic partnerships
- For a safety-focused lab: Safety leadership, alignment research, enterprise trust, reasoning capability
- For a research-first organization: Platform integration, research depth, scientific credibility, multimodal capabilities
Section 3: Strategic Options (Build / Buy / Partner)
Present 3 distinct strategic options. For each:
- Description: What would we do?
- Pros: Why this could win
- Cons: What could go wrong
- Requirements: What capabilities/resources needed
- Timeline: When would we see results
Options should span a range:
- Conservative/Incremental: Low risk, builds on existing strengths
- Moderate/Platform Play: Medium risk, expands the moat
- Ambitious/Moonshot: High risk, could redefine the category
Section 4: The Recommendation
Pick one option (or a phased combination) and defend it:
- What: Crisp description of the strategy
- Why Now: What makes this the right moment
- How: High-level execution roadmap (Phase 1/2/3)
- Who: Key stakeholders and organizational implications
- Moat: How this builds sustainable advantage
- Metrics: How we'd measure strategic success (not just product metrics — market position, ecosystem health, revenue trajectory)
Section 5: Risks & Pre-Mortem
Imagine it's 18 months later and the strategy failed. What went wrong?
- Risk 1: [Most likely failure mode] → Mitigation
- Risk 2: [Highest-impact failure mode] → Mitigation
- Risk 3: [Blind spot / unexpected competitor move] → Mitigation
- Kill criteria: What signals would tell us to pivot?
AI-Specific Strategic Lenses
Always apply these when discussing AI company strategy:
- Capability Trajectory: How do improving model capabilities change this strategy in 6/12/24 months?
- Safety-Capability Frontier: How does this balance pushing capabilities vs. maintaining safety?
- Open vs. Closed: What's the right openness posture? (open-source model weights vs. API-only vs. hybrid)
- Ecosystem Dynamics: How does this affect the developer ecosystem, enterprise customers, and consumer trust?
- Regulatory Landscape: How might AI regulation (EU AI Act, executive orders) affect this?
- Talent Market: How does this affect ability to attract top researchers and engineers?
Output Format
Structure as a strategic analysis — start conversational, then get structured. Aim for ~2500 words. Show your strategic reasoning, not just conclusions.
Research-First Workflow
Before generating the answer:
- Research — Use web search to find latest thinking from AI company blogs, industry analysts, market data, competitor intel. Do 5-10 searches.
- Cite sources — Include
[linked source](url) inline for major claims, data points, and trends.
- Display the complete structured answer.
What Good Looks Like
- Starts with clarifying questions to scope the strategy question
- Shows awareness of where value accrues vs. commoditizes in AI
- Reasons about competitive dynamics specific to AI companies (not generic strategy)
- Presents multiple options before recommending (shows breadth of thinking)
- Recommendation is opinionated and defensible
- Considers second and third-order effects
- Ties strategy back to company mission
- Shows understanding of the AI market structure (models, infrastructure, applications)
