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