How to Use AI as a Product Manager – Google AI Studio & Anti Gravity – Resources

Here’s a detailed summary of the session:

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Detailed Summary: Leveraging AI as a Product Manager (Google AI Studio & Antigravity)

Session Overview

This was a live session attended by approximately 2,000 product managers worldwide (500 on Zoom, 1,500 on YouTube). The session focused on providing both tactical knowledge of AI tools and evergreen mental models for PMs to remain relevant in the AI-driven world.

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Chapter 1: The Product Manager’s Job

The session began by establishing what product managers actually do, broken into three core stages:

1. Product Discovery

• Understanding and discovering what to build
• Activities include: user research, interviews, surveys, trend/market analysis, competitor analysis, stakeholder alignment, problem framing, opportunity sizing, concept development, rapid prototyping, and data-driven decision making
• This is often the most important part of the job at good companies

2. Product Delivery

• Executing and building what was planned
• Product management is a team sport — PMs collaborate with designers, developers, and other stakeholders
• Activities include: creating PRDs, specs, user stories, cross-functional alignment, backlog prioritization, solution design, integration testing, quality management, and continuous improvement
• PMs work more as stakeholder managers than individual contributors

3. Product Distribution (Growth)

• Taking the product to market and ensuring customer adoption
• Activities include: creating Ideal Customer Profiles (ICP), go-to-market strategy, messaging and storytelling, launch execution, user activation and retention, growth experiments, and analytics optimization
• Many PMs mistakenly hand this off entirely to marketing/sales — great PMs stay involved

Key Exercise: Participants were encouraged to observe their own work for two weeks and create a personalized list of their PM responsibilities to identify where AI can help.

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Chapter 2: The AI Landscape

The session introduced the current state of AI tools:

• Tools constantly change (ChatGPT → Claude → Gemini)
• Google is uniquely positioned because they have:
  • Strong infrastructure (TPUs)
  • Massive data layer (Gmail, Docs, Maps, etc.)
  • Product expertise (successful consumer products)
  • End-to-end cloud integration

Google AI Studio was introduced as a “Swiss knife” tool with three main capabilities:

1. Vibe Coding (GenAI Apps) — Build full apps without coding
2. Chat with Models — Strategic thinking partner and document creation
3. Monitor & Manage — Analytics and resource management for deployed apps

Antigravity was introduced as Google’s developer-focused AI coding tool:

• Code editor with AI integration
• Larger context window than competitors
• Visual testing capabilities
• Parallel agent processing

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Chapter 3: The Relationship Between AI and PM (Most Important Section)

The Core Problem: Research shows AI is making people “dumber” — similar to how sedentary desk jobs weakened physical fitness compared to our ancestors who did manual labor.

The Critical Mindset Shift:

Old Thinking	New Thinking
Use AI to save time	Use AI to increase chances of success
Create a 6/10 PRD in 30 minutes	Create a 10/10 PRD in 5 hours
Fast executor	Purpose-driven builder

Key Message: Your purpose as a PM is not to create PRDs or do research — those are means to an end. Your purpose is to solve user problems, achieve business outcomes, and make the world better. AI is a superpower to amplify that purpose, not shortcut it.

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The 3-Layer AI Leverage Stack for Product Managers

Layer 1: AI as Thinking Partner

• Don’t ask AI what to do — ask AI to find your blind spots
• Use cases:
  • Break down unstructured problems
  • Explore blind spots in your thinking
  • Stress test assumptions
  • Simulate stakeholders (create hypothetical user profiles from research data)
• Key question: “How can AI help me think better, not just work faster?”

Layer 2: AI as Acceleration Engine

• Delegate structured, repeatable tasks to AI
• Use cases:
  • Competitive data collection
  • Feature comparison tables
  • Drafting documents (PRDs, personas, journey maps)
  • Feedback summaries (from Play Store, G2, support tickets, etc.)
• Critical reminder: Fast doesn’t mean correct — always build verification steps and human oversight

Layer 3: AI as Product Creator

• Use tools like Google AI Studio, Antigravity, Lovable, Cursor to build actual prototypes
• Good for: Testing, validation, MVPs with limited users
• Not production-ready: Still need to consider privacy, scaling, security for production apps
• Key principle: Treat AI-built prototypes as high-fidelity drafts, not final systems

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Frameworks for Product Managers

The session provided a comprehensive list of PM frameworks to use with AI prompts:

Category	Frameworks
User Research	Mom’s Test, User Persona, User Journey Map, Empathy Map, ICP, Jobs to Be Done
Perspective Analysis	Six Thinking Hats
Market Research	PESTLE, Porter’s Five Forces, SWOT, BCG Matrix
Analytics	AARRR (Pirate Metrics), HEART Framework
Behavioral Design	Hook Framework, BJ Fogg’s Behavior Model
Interview Structure	STAR, CIRCLES
Stakeholder Management	RACI
Prioritization	RICE, Kano Model, MoSCoW

How to use: Reference these frameworks in your AI prompts to get more structured, professional outputs.

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Practical Workflow: Creating Better PRDs

Step 1: Build a “Swipe File”

• Collect great PRD examples from the internet
• Store in Google Drive or Notion
• This becomes your knowledge base

Step 2: Collect Contextual Information

• User research findings
• Market research
• Problem statements
• Business context

Step 3: Generate with AI

• Upload examples + context to Google AI Studio or ChatGPT
• Use the prompt engineering principles (a Cornell research paper was referenced)
• Generate the PRD draft

Step 4: Critique and Iterate

• Use AI (same or different model) to review and rate the PRD
• Identify gaps and improve
• Your own PRDs eventually become part of your swipe file

Important Clarification on “PRD is Dead”: PRDs are NOT dead. Prototypes can show product flow, but PRDs serve as alignment documents explaining:

• Why the product is being built
• How success is measured
• Who the users are
• Dependencies and timelines

PRDs got a bad reputation because people wrote 50-page documents nobody reads. The solution is lean PRDs (1-2 pages), not eliminating them.

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Google AI Studio Walkthrough

Interface Overview:

• Home → Chat with Models (for thinking/documents)
• Home → Build (GenAI Apps for prototypes)
• Model selection, file uploads, voice input available

Key Features Demonstrated:

1. Visual Editor (Annotate Button)
   • Click on any element in the preview
   • Annotate what you want changed
   • Add to chat and regenerate
2. Version History
   • Restore to any previous version
   • Provides user control and safety
3. Export Options:
   • Download as ZIP
   • Push to GitHub
   • Deploy to Google Cloud Run
4. Deployment Process:
   • Click Deploy → Set up Google Cloud billing (free credits available)
   • App gets a public URL
   • Custom domain can be added via Cloud Run → Networking → Custom Domains

Design Inspiration Sources:

• Dribbble.com
• Behance.net
• Figma.com/community

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Antigravity Walkthrough

Key Differentiator from Google AI Studio:

• Runs locally on your computer
• Larger context window (can handle massive codebases)
• Visual testing (automatically tests the UI, not just code errors)
• Parallel agent processing

Interface Components:

1. Agent Manager
   • Spin up multiple AI agents working in parallel
   • Each agent handles different tasks (coding, research, GTM strategy)
   • Inbox shows messages from all agents
2. Editor
   • Traditional code editor with AI assistance
   • Make direct edits to files
3. Artifacts
   • AI creates implementation plans
   • Generates task lists
   • Creates documentation automatically

Live Demo:

• Created a Pomodoro timer app with: duration options, background music, task titles, glassmorphic UI
• Simultaneously ran agents for: GTM strategy creation, market research on productivity apps
• AI automatically tested the app visually and created a walkthrough video

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Practical Exercise

Participants were asked to build an app on Google AI Studio:

• Upload a photo of yourself
• App generates 5-7 versions of that image in different professions (doctor, engineer, lawyer, etc.)
• Inspired by the “3 Idiots” movie scene

Purpose: Learning happens through action and course correction, not passive watching.

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Key Takeaways

1. Mindset over tools — Tools change constantly; the mental framework for using AI effectively is evergreen
2. Don’t be a zombie PM — Stop mindlessly passing instructions from leadership to developers; think critically about your work
3. AI amplifies, doesn’t replace — Your thinking, empathy, intuition, and vision-making remain uniquely human contributions
4. Build a productivity system:
   • Swipe file for examples
   • Prompt database for recurring tasks
   • Chrome extensions to reduce friction
5. Three-layer approach:
   • Thinking Partner (blind spots, stress testing)
   • Acceleration Engine (structured tasks)
   • Product Creator (prototypes, MVPs)
6. Action is essential — Watch tutorials, but more importantly, create your first app today

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Resources Mentioned

• Google AI Studio: aistudio.google.com
• Antigravity: (download required)
• Prompt Engineering Research Paper: Cornell/Stanford collaboration
• Design Inspiration: Dribbble, Behance, Figma Community
• Feedback Tools: Askan.io, Crayon
• HelloPM Program: hellopm.co

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Session Duration: ~2 hours Audience: ~2,000 product managers globally Format: Presentation + Live demos + Hands-on exercise

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