AI Market Landscape

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:

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:

1. 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.
2. Cite everything — Include [linked source](url) inline for all data points.
3. Date the analysis — Include "As of [date]" so the user knows the freshness.
4. 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