/ai-market-landscape

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: 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