What is the AI Market Landscape skill?

A comprehensive, up-to-date competitive landscape analysis of the AI market across models, products, pricing, moats, and positioning.

How fresh is the data?

It emphasizes real-time context and can be refreshed or tuned to current signals; you can specify a time window or request latest updates.

How do I tailor the output to a focus area?

Provide a focus area with an argument-hint or use /ai-market-landscape to constrain analysis to a specific domain (models, enterprise, consumer, agents, etc.).

ai-market-landscape

Scanned

npx machina-cli add skill aroyburman-codes/pm-skills/ai-market-landscape --openclaw

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SKILL.md

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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

Source

git clone https://github.com/aroyburman-codes/pm-skills/blob/main/skills/ai-market-landscape/SKILL.mdView on GitHub

Overview

Provides a comprehensive, up-to-date competitive landscape of the AI market, covering foundation models, products, pricing, moats, and strategic positioning across major labs and emerging players. This context helps AI PMs benchmark moves, identify gaps, and plan product strategy with a clear view of who dominates each layer.

How This Skill Works

It uses the six-section AI Market Landscape framework: The AI Stack, Foundation Model Landscape, Product Landscape, Strategic Dynamics, Market Sizing & Trends, and Implications for Product Decision. The skill ingests signals from labs and startups, maps players across each layer, and outputs a structured brief tailored to the user's focus area.

When to Use It

User asks for the current AI landscape.
User requests a competitive analysis of AI companies.
User needs context on a specific AI market segment (models, agents, enterprise, consumer).
User provides a focus area with /ai-market-landscape command.
Before strategy interviews to build fresh market context.

Quick Start

Step 1: Provide a focus area via argument-hint or /ai-market-landscape [area].
Step 2: Run the six-section framework to generate the landscape map.
Step 3: Review the tailored insights and recommended actions for your product roadmap.

Best Practices

Start by mapping the AI Stack to identify where commoditization is occurring versus where differentiation is strongest.
Benchmark foundation models across key dimensions such as pricing, openness, and enterprise strategy.
Map products by category (Consumer, Developer, Enterprise) and assess moat-building factors.
Regularly refresh the landscape with new announcements, funding signals, and regulatory developments.
Use the argument-hint to constrain outputs to a specific focus area (e.g., models, enterprise, agents) for relevance.

Example Use Cases

Executive briefing comparing OpenAI, Google, Meta, and emerging labs across the six sections.
Foundation model pricing comparison (input/output per 1M tokens) and licensing terms.
Vertical AI moat analysis for healthcare, legal, and finance use cases.
Agent and autonomy trends report highlighting trust, safety, and human-in-the-loop considerations.
Open vs. closed model strategy impact on enterprise deployment and developer loyalty.

Frequently Asked Questions

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