ai-product-teardown

name	ai-product-teardown
description	Structured teardown of AI products (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.). Analyzes product decisions, UX patterns, technical architecture, business model, and competitive positioning.
argument-hint	[product name or feature]

AI Product Teardown Skill

Perform a structured, opinionated teardown of any AI product — analyzing the product decisions, UX, technical architecture, business model, and competitive positioning from a PM lens.

When to Use

• User asks "Tear down [AI product]" or "Analyze [AI product]"
• User wants to understand the product thinking behind an AI feature
• User wants to build product intuition about AI products
• User says /ai-product-teardown followed by a product name
• Great for: ChatGPT, Claude, Gemini, Perplexity, Copilot, Midjourney, Cursor, v0, NotebookLM, etc.

Framework: AI Product Teardown (7 Sections)

Section 1: Product Overview

• What it is: One-sentence description
• Company: Who built it, their mission, and strategic context
• Launch date & trajectory: When launched, key milestones, current scale
• Target users: Primary and secondary audiences
• Business model: How it makes money (or plans to)

Section 2: Core Value Proposition

• Job to be Done: What fundamental job does this product do for users?
• 10x moment: What's the moment where users think "this is magic"?
• Switching cost: What would it take to switch away?
• Network effects: Does it get better with more users? How?

Section 3: UX & Product Decisions

Walk through the key product decisions and evaluate each:

• Onboarding flow: How does a new user go from zero to value?
• Core interaction model: Chat? Canvas? Structured output? Multi-modal?
• Information architecture: How is functionality organized?
• Personalization: How does it adapt to different users?
• Error handling: What happens when the AI is wrong?

For each decision, evaluate:

• What they got RIGHT and why
• What they got WRONG or could improve
• What trade-off they're making (and whether you'd make the same one)

Section 4: Technical Architecture (PM Lens)

Analyze the technical choices from a product perspective:

• Model strategy: Which model(s)? Why that capability level?
• Latency vs. quality trade-off: Where do they sit on the spectrum?
• Context & memory: How does it handle conversation history?
• Safety & guardrails: What's their content policy approach?
• Tool use / plugins / integrations: How extensible is it?
• Pricing architecture: How do technical costs map to pricing?

Section 5: Growth & Distribution

• Acquisition channels: How do users find this? (organic, viral, paid, partnerships)
• Activation: What gets users to the "aha moment"?
• Retention loops: What brings users back?
• Monetization: Free → paid conversion strategy
• Viral mechanics: Does usage naturally create awareness?

Section 6: Competitive Positioning

• Direct competitors: Who else does this job?
• Positioning map: Plot on 2x2 (e.g., capability vs. safety, consumer vs. enterprise)
• Sustainable moats: What's defensible? (data, distribution, brand, model quality, ecosystem)
• Vulnerability: Where could a competitor win?

Section 7: PM Recommendations

If you were the PM, what would you do next?

• Top 3 features to build (with reasoning and expected impact)
• Top 1 thing to kill or change (what's not working)
• Strategic bet: One big swing that could transform the product
• Metrics to watch: What would you track weekly?

Output Format

Write as an opinionated product review — structured but with a clear point of view. Use screenshots/descriptions of specific UI elements where relevant. Aim for ~2000 words. Be specific and cite real features.

Research-First Workflow

1. Research — Search for latest product updates, user reviews, competitor announcements, company blog posts, and usage data. Do 5-10 searches.
2. Cite sources — Include [linked source](url) inline for factual claims.
3. Display the complete teardown.

What Good Looks Like

• Shows you've done homework on the product landscape
• Demonstrates structured product thinking on real products
• Reveals your product taste and judgment
• Provides concrete examples to reference in product discussions
• Builds intuition about AI product patterns across the industry