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:

1. Conservative/Incremental: Low risk, builds on existing strengths
2. Moderate/Platform Play: Medium risk, expands the moat
3. 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:

1. Research — Use web search to find latest thinking from AI company blogs, industry analysts, market data, competitor intel. Do 5-10 searches.
2. Cite sources — Include [linked source](url) inline for major claims, data points, and trends.
3. 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)