{"id":"f393511e-10ce-4734-8636-8316b5cc46fe","shortId":"bb58U2","kind":"skill","title":"product-strategy","tagline":"Structured product strategy framework for AI product roles. Covers: market entry, competitive positioning, build-vs-buy, long-term vision.","description":"# Product Strategy Skill\n\nApply a structured framework to PM product strategy questions targeting AI product roles.\n\n## When to Use\n- User asks \"What strategy would you use for X\"\n- User asks \"How would you enter market X\"\n- User asks \"Define the product strategy for X\"\n- User asks \"How would you decide between X and Y\" (strategic choice)\n- User asks about competitive positioning, market entry, or long-term vision\n- User says `/product-strategy` followed by a question\n- Any question about go-to-market, competitive dynamics, build-vs-buy, or strategic direction\n\n## Context\n- **Tuned for**: AI product roles at frontier AI companies\n- **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.\n- **Common pitfall**: Not landing a clear position. You must identify where the moat is, where commoditization is happening, and connect mission to business goals.\n\n## Framework: Product Strategy (5 Sections)\n\n### Section 1: Strategic Alignment & Clarifications\nAsk 3-5 clarifying questions:\n- **Scope**: Which product line? What timeframe (6mo vs 3yr vs 10yr)?\n- **Constraints**: Are we resource-constrained? What's the competitive urgency?\n- **Success**: What does winning look like? Revenue? Market share? Mission impact?\n- **Context**: Any recent market shifts or company announcements to consider?\n\nState the strategic question clearly in one sentence. Then:\n- **Company Mission**: Restate and connect to the question\n- **Current Position**: Where does the company stand today on this?\n- **Strategic Tension**: What's the core trade-off or decision at the heart of this question?\n\n### Section 2: The Landscape (Market & Leverage)\n**Market Analysis:**\n- Market size (TAM/SAM/SOM) with reasoning\n- Growth rate and trajectory\n- Key trends reshaping the landscape (AI adoption, regulation, platform shifts)\n\n**Competitive Map:**\n- Direct competitors and their positioning\n- Indirect competitors and substitutes\n- Where is the market commoditizing? Where is there differentiation?\n\n**Porter's Five Forces** (applied to AI context):\n- Threat of new entrants (open-source models, startups)\n- Supplier power (compute providers, data sources, talent)\n- Buyer power (enterprise vs consumer, switching costs)\n- Threat of substitutes (alternative approaches, non-AI solutions)\n- Competitive rivalry (between major AI labs and open-source)\n\n**Unique Leverage**: What does THIS company have that others don't?\n- For a model provider with distribution advantage: Consumer product reach, model capability leadership, developer ecosystem, strategic partnerships\n- For a safety-focused lab: Safety leadership, alignment research, enterprise trust, reasoning capability\n- For a research-first organization: Platform integration, research depth, scientific credibility, multimodal capabilities\n\n### Section 3: Strategic Options (Build / Buy / Partner)\nPresent 3 distinct strategic options. For each:\n- **Description**: What would we do?\n- **Pros**: Why this could win\n- **Cons**: What could go wrong\n- **Requirements**: What capabilities/resources needed\n- **Timeline**: When would we see results\n\nOptions should span a range:\n1. **Conservative/Incremental**: Low risk, builds on existing strengths\n2. **Moderate/Platform Play**: Medium risk, expands the moat\n3. **Ambitious/Moonshot**: High risk, could redefine the category\n\n### Section 4: The Recommendation\nPick one option (or a phased combination) and defend it:\n- **What**: Crisp description of the strategy\n- **Why Now**: What makes this the right moment\n- **How**: High-level execution roadmap (Phase 1/2/3)\n- **Who**: Key stakeholders and organizational implications\n- **Moat**: How this builds sustainable advantage\n- **Metrics**: How we'd measure strategic success (not just product metrics — market position, ecosystem health, revenue trajectory)\n\n### Section 5: Risks & Pre-Mortem\nImagine it's 18 months later and the strategy failed. What went wrong?\n- **Risk 1**: [Most likely failure mode] → Mitigation\n- **Risk 2**: [Highest-impact failure mode] → Mitigation\n- **Risk 3**: [Blind spot / unexpected competitor move] → Mitigation\n- **Kill criteria**: What signals would tell us to pivot?\n\n## AI-Specific Strategic Lenses\n\nAlways apply these when discussing AI company strategy:\n- **Capability Trajectory**: How do improving model capabilities change this strategy in 6/12/24 months?\n- **Safety-Capability Frontier**: How does this balance pushing capabilities vs. maintaining safety?\n- **Open vs. Closed**: What's the right openness posture? (open-source model weights vs. API-only vs. hybrid)\n- **Ecosystem Dynamics**: How does this affect the developer ecosystem, enterprise customers, and consumer trust?\n- **Regulatory Landscape**: How might AI regulation (EU AI Act, executive orders) affect this?\n- **Talent Market**: How does this affect ability to attract top researchers and engineers?\n\n## Output Format\nStructure as a strategic analysis — start conversational, then get structured. Aim for ~2500 words. Show your strategic reasoning, not just conclusions.\n\n## Research-First Workflow\nBefore generating the answer:\n1. **Research** — Use web search to find latest thinking from AI company blogs, industry analysts, market data, competitor intel. Do 5-10 searches.\n2. **Cite sources** — Include `[linked source](url)` inline for major claims, data points, and trends.\n3. **Display** the complete structured answer.\n\n## What Good Looks Like\n- Starts with clarifying questions to scope the strategy question\n- Shows awareness of where value accrues vs. commoditizes in AI\n- Reasons about competitive dynamics specific to AI companies (not generic strategy)\n- Presents multiple options before recommending (shows breadth of thinking)\n- Recommendation is opinionated and defensible\n- Considers second and third-order effects\n- Ties strategy back to company mission\n- Shows understanding of the AI market structure (models, infrastructure, 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