Feature Factories Build AI Wrappers, Product Orgs Build Moats
Every company can call the OpenAI API. Every developer can wrap Claude in a decent UI. Every product team can ship “AI-powered” features in a sprint or two.
The hard part isn’t adding AI. It’s building something users can’t easily replicate elsewhere.
The Wrapper Trap
Integrating GPT-5 or Claude takes a weekend. Polish the UI, tune some prompts, add it to your feature list. Congratulations—you’ve built what a hundred competitors can build in the same timeframe.
Users commoditize AI features instantly. If your “AI-powered writing assistant” offers the same value as ChatGPT with better instructions, why wouldn’t they just use ChatGPT? If your meeting summarizer works like every other meeting summarizer, you’re competing on price and distribution alone.
Grammarly spent years building writing data, style guides, and brand trust. Then ChatGPT offered similar writing assistance for free. Their remaining moat? Enterprise IT relationships and procurement inertia. That’s the lesson: yesterday’s data moats don’t automatically transfer to the LLM era.
What Actually Creates Moats
Real differentiation comes from what you build around the AI:
Proprietary data flywheels. Your system gets smarter from user interactions—trained on their workflows, their terminology, their edge cases. The value compounds with usage and can’t be copied by competitors starting from scratch. Real data moats require uniqueness, structure, and continuous feedback loops.
Deep workflow integration. AI embedded in tools users already depend on creates switching costs. GitHub Copilot works because it’s native to VS Code, understands repo context, and fits developer workflow. A standalone AI code editor faces an uphill adoption battle.
Trust infrastructure. SOC2 compliance, HIPAA certification, audit trails, data residency guarantees. These take 6-12 months to build properly and can’t be copied overnight. Enterprise buyers care more about this than model performance.
Domain expertise encoded. Generic prompts produce generic results. Real value comes from vertical knowledge baked into your system—industry terminology, regulatory requirements, workflow patterns that took months to map.
B2B Moats Look Different
Consumer AI wrappers commoditize in weeks. B2B wrappers have more time because of procurement cycles—but without real integration, they’ll commoditize too. Just slower. The squeeze forces startups to sequence distribution into defensibility before incumbents close the window.
System integration creates defensibility. APIs that connect to Salesforce, Jira, Slack, and internal tools take months to build and certify. Users don’t switch because removal disrupts workflows across multiple systems.
Switching costs through team adoption matter more than individual user preferences. When entire teams are trained on your tool, share templates and configurations, collaborate through your platform—that’s organizational lock-in, not just feature preference.
Professional services create procurement moats. If implementation requires weeks of configuration, change management, and custom integration work, you’re selling transformation, not software. ChatGPT Enterprise can’t replace that.
The Test
Can a user get identical value from ChatGPT Enterprise with custom instructions? If yes, you’re in the wrapper business. And that’s a race to zero margin.
Can a competitor replicate your core value in a quarter? If yes, your moat is brand and distribution, not product. Those erode faster in the AI era.
The window for experimentation without consequences is closing. Users are developing AI feature fatigue. Capital is flowing to companies with actual defensibility. The gap between wrapper products and moat products is widening every quarter.
The question isn’t whether to ship AI features. It’s whether you’re building something users can only get from you.
What makes your AI defensible?