{"id":"f9726482-6c10-490d-b8e5-a0fa2429e906","shortId":"za6vKN","kind":"skill","title":"ai-market-landscape","tagline":"Real-time competitive analysis of the AI market. Covers foundation models, products, pricing, moats, and strategic positioning across major AI labs and emerging players.","description":"# AI Market Landscape Skill\n\nGenerate a comprehensive, up-to-date analysis of the AI competitive landscape — the market context every AI PM needs.\n\n## When to Use\n- User asks \"What's the current AI landscape?\"\n- User wants a competitive analysis of AI companies\n- User needs context on a specific AI market segment (models, agents, enterprise, consumer)\n- User says `/ai-market-landscape` followed by a focus area\n- Before any strategy interview to build fresh market context\n\n## Framework: AI Market Landscape (6 Sections)\n\n### Section 1: The AI Stack (Where Value Accrues)\n\nMap the current AI value chain:\n\n```\nLayer 5: Applications    (ChatGPT, Perplexity, Cursor, vertical SaaS)\nLayer 4: Orchestration   (LangChain, agent frameworks, MCP)\nLayer 3: Models          (GPT-4, Claude, Gemini, Llama, Mistral)\nLayer 2: Infrastructure  (AWS, Azure, GCP, Together, Fireworks)\nLayer 1: Compute         (NVIDIA, AMD, custom chips - TPU, Trainium)\n```\n\nFor each layer:\n- Who are the key players?\n- Where is commoditization happening?\n- Where is differentiation strongest?\n- Where is the most value being captured today vs. in 2 years?\n\n### Section 2: Foundation Model Landscape\n\nCompare the major model providers:\n\n| Dimension | Lab A | Lab B | Lab C | Lab D | Lab E |\n|-----------|--------|-----------|--------|------|---------|\n| Latest model | | | | | |\n| Key capability | | | | | |\n| Pricing (input/output per 1M tokens) | | | | | |\n| Open vs. closed | | | | | |\n| Primary distribution | | | | | |\n| Enterprise strategy | | | | | |\n| Safety approach | | | | | |\n| Funding / valuation | | | | | |\n\n### Section 3: Product Landscape\n\nMap AI products by category:\n\n**Consumer AI:**\n- General assistants (ChatGPT, Claude, Gemini)\n- Search (Perplexity, SearchGPT, Gemini)\n- Creative (Midjourney, DALL-E, Suno, Runway)\n- Productivity (Notion AI, Copilot, Jasper)\n\n**Developer AI:**\n- Code (Cursor, GitHub Copilot, Claude Code, Windsurf)\n- APIs & platforms (major LLM provider APIs, cloud AI platforms)\n- Infrastructure (Vercel AI SDK, LangChain, LlamaIndex)\n\n**Enterprise AI:**\n- Horizontal (Microsoft Copilot, Google Workspace AI, Salesforce Einstein)\n- Vertical (Harvey for law, Abridge for healthcare, Palantir AIP)\n\n**Agents & Automation:**\n- Computer use agents (browser and desktop automation)\n- Workflow automation (Make, Zapier AI, n8n)\n- Autonomous coding (Devin, Claude Code, Codex)\n\n### Section 4: Strategic Dynamics\n\nAnalyze the key strategic questions shaping the market:\n\n**Open vs. Closed:**\n- Open-weight model strategies vs. closed-model approaches\n- Impact on commoditization, developer loyalty, enterprise adoption\n- Where does open-source win? Where does it lose?\n\n**Consumer vs. Enterprise:**\n- Consumer-first strategies (chatbot → enterprise upsell)\n- Enterprise-first strategies (API → consumer product)\n- Google's distribution advantage (Android, Chrome, Workspace, Search)\n\n**Horizontal vs. Vertical:**\n- Can horizontal AI products win vertical use cases?\n- When do vertical AI startups have a wedge?\n- The data moat question: does proprietary data still matter?\n\n**Agents & Autonomy:**\n- Where is agentic AI working today vs. hype?\n- Trust and safety challenges with autonomous agents\n- The \"human-in-the-loop\" spectrum\n\n### Section 5: Market Sizing & Trends\n\n**Current market data** (research the latest):\n- Total AI market size and growth rate\n- AI infrastructure spend\n- Enterprise AI adoption rates\n- Consumer AI MAU trends\n- Developer tool market\n\n**Key trends to track:**\n- Model capability improvement curves\n- Price per token trajectory (deflationary)\n- Multimodal adoption\n- AI regulation (EU AI Act, US executive orders)\n- AI talent market dynamics\n\n### Section 6: Implications for Product Decisions\n\nBased on the landscape, highlight:\n- **Key questions** each company is wrestling with right now\n- **Strategic tensions** shaping product roadmaps\n- **Product opportunities** where each company has a gap\n- **Open debates** in the AI product community\n\n## Output Format\nWrite as an analyst briefing — data-driven, opinionated, and actionable. Use tables for comparisons. Include specific numbers and sources. Aim for ~2500 words.\n\n## Research-First Workflow (CRITICAL)\nThis skill is ONLY valuable with fresh data:\n1. **Research extensively** — Do 10-15 web searches covering: latest model releases, funding rounds, product launches, market reports, earnings calls, developer surveys, and thought leader commentary.\n2. **Cite everything** — Include `[linked source](url)` inline for all data points.\n3. **Date the analysis** — Include \"As of [date]\" so the user knows the freshness.\n4. **Display** the complete landscape analysis.\n\n## What Good Looks Like\n- Demonstrates you follow the AI market closely\n- Shows you understand competitive dynamics beyond surface level\n- Provides specific data points to drop in strategy discussions\n- Reveals understanding of where value accrues vs. commoditizes\n- Builds the context needed for \"what would you build?\" 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