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Greetings from above,
Remember that "brilliant" AI chatbot we launched last quarter? The one that was supposed to revolutionize customer support?
Yeah, it ended up costing us a fortune in API calls and mostly just confused people.
It felt like trying to build a spaceship with duct tape and wishful thinking.
That failure sent me down a rabbit hole.
I realized we were just slapping AI onto our product like a cheap sticker, with no real strategy.
After learning from product leaders at places like OpenAI, I figured out the difference between a real AI product and a glorified tech demo.
It changed everything.
Today, we'll talk about:
Why your AI initiatives are getting zero return.
The only three AI moats that actually matter.
A 5-phase system for building a real AI product strategy.
Let's explore!
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💬 THIS WEEK'S READER QUESTION
"We've built a few AI prototypes, but none of them feel like a real business. It’s just a collection of cool features that don't connect. How do we move from scattered experiments to a focused AI product strategy that actually creates value and defensibility?"
This is the million-dollar question. So many teams are stuck in "AI experiment mode." Slack channels are buzzing, demos look cool, but nothing sticks.
It’s because they’re confusing features with a strategy. An "AI assistant" isn't a business, it's a novelty. Let's fix that.
🎯 Building an AI-Powered Product, Not Just AI Features 🎯
Most organizations are getting zero return from their AI investments.
Why? Because they’re adding AI features instead of building AI-powered products.
An AI product isn’t about a "summarize" button. It’s about rethinking the product from the ground up so that AI becomes its engine—invisible, essential, and compounding in value as you grow.
🔥 Phase 1: Direction - Choosing the Right Moat 🔥
Forget what model you're using. Models are temporary; moats are permanent. Relying on GPT-5 is like renting land. You need to own something deeper. In AI, only three moats matter.
1. The Data Moat
If your product generates unique, high-quality data with every use, you're building a real asset. This data can be used to train better, cheaper, and more accurate models that no competitor can buy.
Example: Duolingo used a decade of data on how millions of people learn to fine-tune their AI, creating a learning path no one else could replicate.
2. The Distribution Moat
Even the best AI tool is useless if no one sees it. Owning the workflow and user base is a massive advantage.
Example: Notion AI succeeded because it was instantly available to tens of millions of users who were already in the product. Distribution was instantaneous.
3. The Trust Moat
AI fails. It hallucinates. The biggest barrier to adoption is often trust, not accuracy.
Example: Microsoft Copilot wins in the enterprise not because its tech is dramatically better, but because Microsoft guarantees security, compliance, and governance.
Key benefits:
Creates long-term defensibility.
Builds a compounding advantage.
Moves you from a commodity to a necessity.
⚙️ PROMPT EXAMPLES ⚙️
🔥 Phase 1: Identify Your Moat 🔥
This prompt helps you analyze your business and identify the most viable AI moat to build for your product.
Why this matters:
Forces you to think beyond just the AI model.
Clarifies your long-term strategic direction.
Helps you build a defensible business, not just a feature.
Moat Identifier Prompt:
Adopt the role of an expert AI-Era Startup Architect. Your mission is to help me identify the strongest potential moat for my AI product.
Before you begin, ask me these questions one by one:
1. **Product/Service Idea:** Briefly describe your AI product or service. What problem does it solve?
2. **User Interaction Data:** What unique data is generated every time a user interacts with your product? Is it structured and high-quality?
3. **Current Distribution:** How do you currently get your product in front of users? Do you have an existing user base, platform integrations, or key partnerships?
4. **Criticality & Trust:** How critical is your product's output to the user's workflow? What are the consequences of an error or hallucination?
Based on my answers, analyze my situation and recommend which of the three AI moats (Data, Distribution, or Trust) I should focus on building. Provide a step-by-step rationale for your recommendation and give three actionable steps I can take in the next 90 days to start building that moat.Perfect for:
Founders starting a new AI venture.
Product Managers defining their AI strategy.
Anyone feeling their AI product lacks a real competitive edge.
Implementation tip: Be brutally honest about your current state. Don't overestimate your data quality or distribution channels. Clarity is key.
📤 EXAMPLE OUTPUT 📤
🔥 Phase 2: Differentiation - Standing Out 🔥
When everyone can call the same API, your model isn't an advantage. The winners will be those who create systems of differentiation. Here are four levers that work: Workflow Integration, UX Scaffolding, Domain-Specific Context, and Community & Ecosystem.
2. The Differentiation Identifier
This prompt helps you choose and focus on the single most powerful way to stand out.
Why this matters:
Prevents you from becoming another commodity "wrapper."
Focuses your resources on what will make you unique.
Builds a brand that users choose for a specific reason.
Assume the role of a seasoned AI Product Strategist. I will describe my AI product. Your task is to help me identify a primary differentiation lever.
My Product: [Describe your AI product, its users, its core function, and its primary moat from Phase 1]
Based on my product description, analyze the four differentiation levers (Workflow Integration, UX Scaffolding, Domain-Specific Context, Community & Ecosystem) and recommend the SINGLE most powerful one for me to focus on. Explain why you chose it and provide three specific, actionable ideas for how to implement it.🔥 Phase 3: Design - Building The Product Architecture 🔥
AI products are not SaaS products. Every user interaction costs you money. Your product's architecture—how you structure data flows, model usage, and user interactions—is the difference between scaling profitably and dying under your own success.
3. The Architecture Auditor
This prompt helps you choose the right product pattern and forces you to think about costs from day one.
Why this matters:
Prevents you from building a product that is unprofitable at scale.
Clarifies the core user interaction model (assistive vs. autonomous).
Instills cost-conscious design principles in your team.
Adopt the role of an AI Product Architect with deep expertise in unit economics. My goal is to design a profitable AI product.
My Product Idea: [Describe the AI feature or product, the user workflow it fits into, and how frequently you expect users to interact with the AI.]
Based on this, your task is to:
1. **Recommend a Product Pattern:** Should this be a Copilot (assistive), Agent (autonomous), or Augmentation (embedded) pattern? Justify your choice based on user trust and potential cost implications.
2. **Identify Cost Drivers:** List the top 3-5 factors that will drive my inference costs (e.g., prompt length, frequency of use, model choice).
3. **Propose Cost-Saving Measures:** Suggest three concrete design or technical choices I can make to control these costs without harming the user experience.🔥 Phase 4: Deployment - Scaling Without Breaking Costs 🔥
The paradox of AI is that adoption can kill you. Deployment isn't about launching big; it's about designing a growth engine that balances user growth, cost efficiency, and building your moat.
4. The Scalable Deployment Planner
This prompt creates a phased rollout strategy that prevents runaway costs and ensures your product gets smarter as it scales.
Why this matters:
Avoids the "launch big and go bankrupt" problem.
Builds a sustainable growth flywheel.
Ensures every new user makes your product better, not just more expensive.
Act as a Head of AI Product who has scaled multiple products from zero to millions of users. I need a deployment plan for my new AI feature.
My Feature: [Describe the feature, its current stage (e.g., prototype, beta), and the primary moat we are trying to build (Data, Distribution, or Trust)]
Create a 3-stage deployment plan:
1. **Stage 1: Controlled Pilot.** Who is the target user group (e.g., 5% of power users)? What are the key cost and feedback metrics to collect over 2-4 weeks?
2. **Stage 2: Gradual Rollout.** What levers will you use to control the adoption curve (e.g., waitlist, credit system, tiered access)? How will you use this phase to strengthen the moat?
3. **Stage 3: Full-Scale Launch.** What are the specific cost, performance, and moat-related KPIs that must be met before we release to all users?🔥 Phase 5: Leadership - Embedding AI Into the Org 🔥 Phase 5: Leadership - Embedding AI Into the Org 🔥
Your final job is to make AI a durable part of your company's DNA, not just a shiny project. This requires a shift in mindset from shipping features to designing systems and getting executive buy-in with ROI, not hype.
5. The AI Leadership Playbook
This prompt helps you articulate your strategy to the rest of the organization and build a culture of disciplined innovation.
Why this matters:
Secures organizational buy-in and resources.
Prevents the chaos of random, disconnected AI experiments.
Aligns the entire company around a single, coherent AI vision.
Assume the role of an AI-savvy Chief Product Officer. I am a product leader trying to embed a strategic AI approach in my organization.
My biggest challenge is: [Choose one: Getting executive buy-in, stopping scattered/chaotic experiments, or aligning the org around our AI moat strategy]
Based on my challenge, provide a tailored mini-playbook that includes:
1. **A Core Narrative:** A simple, powerful story I can tell to explain our approach (e.g., "We are building a data flywheel," or "We are becoming the trusted copilot for our industry").
2. **Key Talking Points:** Three bullet points focused on business outcomes (ROI, defensibility, user retention), not tech hype.
3. **A Structural Change:** One specific process to implement (e.g., a mandatory 2-week AI sprint for new ideas, a monthly AI strategy review with the C-suite).💡 PROMPT TIP OF THE WEEK: Hypothesis-Driven Experiments 💡
Stop just "trying stuff" with AI. Run structured experiments.
Before:
Let's see if we can use an LLM to improve customer support.After:
Adopt the role of a Product Manager running a structured AI experiment.
My Hypothesis is: "If we use an AI to auto-draft customer support replies based on our internal knowledge base, we can reduce the average ticket resolution time by 20% within a 2-week sprint without lowering our Customer Satisfaction Score (CSAT)."
My Core Task:
Create a lean, 2-week experiment plan to validate this hypothesis.
Your Output Should Include:
1. **MVP Definition:** What is the absolute smallest thing we can build to test this?
2. **Key Metrics:** What are the primary and secondary metrics we must track (e.g., resolution time, CSAT, agent adoption rate, hallucination rate)?
3. **User Group:** Who will be part of the pilot test?
4. **Kill/Scale Criteria:** At the end of the 2 weeks, what specific results would lead us to KILL, ITERATE, or SCALE this feature?This structured approach moves you from a vague idea to a disciplined experiment with clear success and failure criteria, saving you months of wasted effort.
📋 SUMMARY 📋
Most AI initiatives fail because they lack a real strategy. Stop adding features and start building AI-powered products.
Your moat is your only long-term defense. Choose to dominate on Data, Distribution, or Trust.
Differentiation is how you win today. Focus on Workflow, UX, Domain Context, or Community.
📚 FREE RESOURCES 📚
📦 WRAP UP 📦
What you learned today:
Build Moats, Not Features: Your survival depends on having a defensible advantage in Data, Distribution, or Trust.
Differentiate or Die: In a world of API wrappers, your unique value comes from how you integrate AI into workflows and build a trusted user experience.
Experiment with Discipline: Don't just play with AI. Run structured sprints with clear hypotheses and metrics to avoid wasting time and money.
Stop being a PM who "adds AI" to the backlog. Become the leader who sets the company's direction in an AI-first world.
What did you think about today's edition?
And as always, thanks for being part of my lovely community,
Keep learning,
🔑 Alex from God of Prompt
P.S. What's the biggest AI strategy mistake you've seen a company make?
Reply and let me know!
