I counted up the featured AI products on Product Hunt last week and found that 18 out of 20 were essentially just thin wrappers around ChatGPT or Claude APIs.
My immediate first reaction was dismissive: “These companies have no defensible moat whatsoever.” Then I realized I was literally writing this article on an Apple computer, which is largely a wrapper around chips manufactured in Taiwan, an operating system that wraps Unix, and components sourced from a dozen different suppliers around the world.
Wrappers are absolutely everywhere in the economy. They’re called intermediaries or distributors. This is literally how most of the modern economy actually works.
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Stripe Is Just a Wrapper
Stripe is currently valued at $65 billion dollars. So what exactly do they do? They wrap banks and payment processors. You integrate with Stripe’s API once instead of integrating with 47 different payment providers across different countries, each with completely different APIs, compliance requirements, and failure modes.
But Stripe adds massive value in multiple ways: better developer experience through great documentation and a simple unified API, single integration point instead of dozens, abstracted complexity where you don’t have to worry about PCI compliance, significantly better reliability than dealing with banks directly, and incredible distribution where they’re the default payment choice for startups.
This isn’t “just” a wrapper. It’s a genuinely valuable business solving real problems.
Everyone Wraps Something
Gas stations don’t actually refine the gasoline they sell, they just distribute it from refineries to consumers. Apple doesn’t fabricate the chips that power their devices, they design the architecture and distribute the final products. Nobody complains that these are “just wrappers” even though that’s exactly what they are.
This is just basic economics in action: specialization and trade creates more value than everyone trying to do everything themselves. Vertical integration isn’t always the optimal business strategy. Sometimes the very best business model is being absolutely great at one specific layer of the value chain.
Why Wrappers Exist
Good intermediaries add genuine value to the market in several ways: they provide distribution to customers the underlying provider can’t reach, offer a better user interface or developer experience, aggregate multiple services into one, customize solutions for specific use cases, and build trust through reliability and compliance guarantees.
Bad intermediaries just mark up the price without adding any real value, which is classic rent-seeking behavior that eventually gets competed away.
The important question isn’t “is this product just a wrapper?” The real question is “what specific value does this wrapper actually add for customers?”
Wrappers That Work
Cursor wraps Claude and GPT-4 but adds seamless IDE integration, automatic codebase context, and a purpose-built user experience specifically designed for coding workflows. You could technically use Claude directly for coding, but Cursor makes the entire process dramatically faster and more efficient.
Perplexity wraps various LLMs but adds search-specific prompting techniques, automatic source citation for fact-checking, and a research-focused user interface that’s optimized for finding and verifying information.
GitHub Copilot wraps OpenAI models but adds deep integration with your IDE, code-specific training data, and enterprise features like security scanning and compliance. Microsoft’s massive distribution advantage combined with OpenAI’s powerful models creates something genuinely powerful.
Notion AI wraps general LLMs but adds the critical context of your entire workspace, including all your documents, databases, and team knowledge. The real value is having AI that already knows everything about your company’s specific data and processes.
Wrappers That Struggle
Generic “ChatGPT but for X” products are fundamentally weak: If your entire value proposition is “ChatGPT with some pre-written system prompt templates,” that’s an incredibly thin moat. Users will just use ChatGPT directly and write their own prompts, which takes about five minutes to learn.
Just adding a prettier UI isn’t nearly enough: Claiming you have a “better UI on Claude” doesn’t work when Claude already has a perfectly functional UI that Anthropic continuously improves with a team of professional designers. Your slightly nicer interface isn’t defensible against the company that owns the actual model.
Having no specialized data means no moat: If you’re not adding proprietary context, domain-specific data, or specialized training on top of the base model, what actually stops your users from just using the base model directly and saving the markup you’re charging?
What Actually Matters
You should evaluate AI wrapper products based on these specific criteria:
Distribution: Can you reach customers that the base LLM provider can’t access directly? Integration: Do you provide access to systems and data that the base LLM doesn’t have? Specialization: Are you genuinely better for specific use cases than the general-purpose model? Data: Do you add proprietary context or domain knowledge that isn’t publicly available? UX: Is your interface dramatically better, not just slightly prettier? Trust: Do you provide SLAs, compliance guarantees, or reliability that the base model doesn’t?
If you can’t honestly answer yes to at least one of these questions, you don’t actually have a defensible business.
When to Build a Wrapper
You should build an AI wrapper if you already have one of these significant advantages: existing distribution to customers who don’t use the base models, deep domain expertise in a specific field, proprietary data that adds unique value, integration advantages with existing systems, or genuine UX innovation that dramatically improves the experience.
Don’t build an AI wrapper if your only value proposition is marking up API calls without adding anything else meaningful.
McDonald’s definitely doesn’t make the best burgers in the world, but they have 40,000 locations providing incredible distribution. Microsoft doesn’t make the best AI models, but they control Office, Windows, and GitHub which gives them unmatched distribution. Distribution itself can be the entire moat.
Specialized wrappers add massive value in specific domains: legal AI that deeply understands contract law and specific clause types, medical AI that knows detailed billing codes and insurance requirements, financial AI that’s trained extensively on SEC filings and regulatory documents. General-purpose LLMs are reasonably good at everything but genuinely great at nothing specific.
Bottom Line
I honestly don’t care whether an AI product is technically a wrapper around someone else’s API. What I actually care about is whether it’s genuinely useful for solving my specific problems.
Cursor makes me code significantly faster than using Claude directly. Perplexity gives me better research results with proper citations. Notion AI already has all my company’s context built in. These products deliver real value that justifies their existence.
Stripe is fundamentally a wrapper around banks. Gas stations are wrappers around refineries. Most successful businesses in the economy are intermediaries adding value between producers and consumers.
What actually matters is the specific value you add to the chain: better distribution that reaches new customers, deeper integration with existing workflows, dramatically better user experience, true specialization for specific use cases, or proprietary data and context.
If you’re genuinely adding at least one of those things, you’re not “just” a wrapper. You’re a valuable participant in the AI value chain providing real benefits to customers.
That’s a real business worth building.