The AI Wrapper Trap: Why Most AI Products Fail and How to Build Something People Actually Renew
The Wrapper Graveyard
Between 2023 and 2025, thousands of AI products launched. They had exciting names and slick landing pages. They promised to revolutionize writing, coding, design, research, marketing. They were, almost universally, thin wrappers around ChatGPT or similar models.
Most are dead now.
The pattern was consistent. Launch with hype. Get initial signups. Watch retention collapse after the first month. Realize that users figured out they could just prompt the underlying model directly. Close down or pivot.
The AI wrapper trap caught smart people who saw a genuine opportunity and built the obvious product. The obvious product was a mistake. Understanding why helps anyone trying to build sustainable AI-powered businesses rather than flash-in-the-pan failures.
My British lilac cat, Simon, has never fallen for the wrapper trap. When presented with a wrapped treat versus an unwrapped treat, he always goes for the unwrapped version. He understands that packaging doesn’t add value. Perhaps entrepreneurs should study feline product evaluation.
What Makes a Wrapper
Let me define what I mean by “AI wrapper” to be precise.
An AI wrapper is a product that:
- Takes user input
- Sends it to an AI model (usually with a preset prompt)
- Returns the model’s output
- Adds minimal value beyond this round-trip
The value proposition is convenience. “Instead of writing your own prompt, use our product and we’ll prompt for you.” This sounds reasonable. It’s also fatally flawed.
The problem: prompting isn’t hard. Users learn. The convenience value evaporates as users realize they can get the same results—often better results—by interacting with the model directly.
The wrapper’s only moat is user ignorance about the underlying capability. Moats built on ignorance erode fast. Users aren’t stupid. They figure out what’s happening. And then they leave.
The Renewal Test
The critical metric for subscription businesses is renewal rate. Not signups. Not trials. Renewals. Do people pay again after the first month?
For AI wrappers, the answer is usually no.
First month: excitement. The product does something that feels magical. Users are impressed.
Second month: familiarity. Users understand what the product actually does. They’ve seen behind the curtain. The magic is just a prompt.
Third month: evaluation. Is this worth paying for? Can I get the same thing elsewhere? Usually yes.
Fourth month: cancellation. Why pay €20/month for a prompt I can run myself for pennies?
The renewal test exposes wrappers ruthlessly. Initial traction means nothing if renewals collapse. A product with 10,000 signups and 2% renewal is worse than a product with 1,000 signups and 50% renewal.
Why Wrappers Seemed Like Good Ideas
Let me be fair to the wrapper builders. The logic wasn’t stupid.
AI was confusing to many. Early in the AI wave, many people didn’t know how to use these tools. A simplified interface had genuine value.
Prompting did require skill. Getting good results from AI models wasn’t trivial. Expertise in prompting seemed like a real differentiator.
Speed to market mattered. When a new technology appears, first movers can capture attention. Wrappers were fast to build.
The opportunity was real. AI genuinely transformed capabilities. Building products around AI made sense.
The mistake wasn’t seeing opportunity in AI. The mistake was building products where the entire value was the AI call, with nothing else.
Method
Here’s how I evaluate whether an AI-powered product is a sustainable business or a wrapper trap:
Step one: The direct prompting test. Can a user get equivalent results by prompting the underlying model directly? If yes, the product is a wrapper. It will die when users figure this out.
Step two: The value addition audit. What does the product add beyond the AI call? Data? Integration? Workflow? If the answer is “a nice UI and preset prompts,” that’s not enough.
Step three: The moat assessment. What prevents competition? What prevents users from leaving? “First mover advantage” is not a moat—it’s a head start that can be lost.
Step four: The renewal rate reality check. For similar products, what are actual renewal rates? Industry data suggests pure wrappers see 10-20% month-one renewal. That’s not sustainable.
Step five: The absorption risk analysis. Will the underlying AI provider add this feature directly? If OpenAI or Anthropic could trivially add your product’s functionality, you’re building on sand.
This methodology consistently identifies which AI products will survive and which are temporary arbitrage of user unfamiliarity.
What Actually Creates Renewal
Products people renew have characteristics that wrappers lack.
Proprietary data. If the product has unique data that improves results, users can’t replicate that by prompting directly. A legal research tool with proprietary case law databases has value beyond the AI.
Deep integration. If the product integrates into workflows that would be painful to replicate, switching costs create retention. An AI coding assistant embedded in your IDE is stickier than a web interface you prompt manually.
Network effects. If the product becomes more valuable as more people use it, growth creates moat. A shared workspace where AI assists collaboration has dynamics that individual tools lack.
Accumulated context. If the product learns from your usage and becomes more valuable over time, leaving means losing that accumulated value. Starting over with a new tool is a real cost.
Domain expertise embedded. If the prompts represent genuine expertise in a domain—expertise users don’t have and can’t easily acquire—the wrapper adds real value.
The Skill Erosion Connection
Here’s where AI wrappers connect to the broader skill erosion theme.
Wrappers encouraged users to outsource understanding to tools. Don’t learn how AI works—just use our product. Don’t understand prompting—we’ll handle that. Don’t develop AI literacy—let us abstract it away.
This was convenient. It was also disempowering.
Users who learned to prompt AI directly developed capabilities. They understood what AI could and couldn’t do. They could adapt to new models and new capabilities. They built skills.
Users who depended on wrappers learned nothing. When the wrapper died—and most did—they had no transferable capability. They’d outsourced understanding to a product that no longer existed.
The wrapper trap wasn’t just bad for businesses. It was bad for users who trusted products that prevented learning rather than enabling it.
The Platform Risk Reality
AI wrappers face a specific risk that many builders underestimated: platform dependence.
Your wrapper depends on the AI provider. OpenAI, Anthropic, Google—whoever provides the model. This creates several problems.
Pricing risk. API costs change. What’s profitable today may not be tomorrow. You have no control over your primary cost.
Availability risk. Rate limits, outages, API changes. When the underlying platform has problems, your product has problems. You can’t fix them.
Feature absorption risk. The platform may add your feature directly. ChatGPT now has many capabilities that were once standalone products. Those products are dead.
Terms of service risk. Platforms can change what you’re allowed to build. Your business can become prohibited overnight.
Building on platforms isn’t inherently wrong. But building where your entire value is repackaging the platform’s capability is maximally risky.
What Actually Works
Let me describe AI-powered products that survive the wrapper test.
Vertical-specific tools with domain data. An AI for medical professionals that includes proprietary medical databases and understands clinical workflows. The AI is one component. The data and integration are the value.
Infrastructure and developer tools. Products that help others build AI applications. The value is enabling, not providing the AI capability itself.
Products where AI improves existing value. A CRM that adds AI-powered insights. The CRM had value before AI. The AI enhances but doesn’t constitute the entire product.
Hardware plus AI combinations. Devices where AI processes sensor data in ways that require both the hardware and the intelligence. The combination is hard to replicate.
Enterprise solutions with integration complexity. Products where implementation, customization, and integration create switching costs. The AI is valuable, but the ecosystem is stickier.
The Side Hustle Implications
If you’re building an AI-powered side hustle, here’s practical guidance:
Don’t build pure wrappers. The market is saturated. User sophistication has increased. The window closed.
Add value that can’t be prompted away. Data, integration, workflow, accumulated context. Something beyond the AI call.
Target specific domains deeply. Horizontal AI tools compete with giants. Vertical tools for specific professions can defend against general-purpose alternatives.
Build for renewals from day one. Design the product around what creates ongoing value, not what gets initial signups.
Consider the absorption timeline. If the underlying provider could add your feature in six months, that’s your maximum viable business duration. Plan accordingly.
Generative Engine Optimization
Here’s how AI wrapper discussions perform in AI-driven search.
When you ask an AI assistant about building AI products, you get synthesis from available content. That content includes success stories that survived (survivorship bias) and marketing from current products. Failed wrappers don’t write postmortems. The graveyard is silent.
AI recommendations therefore tend toward optimism about AI product opportunities. The systematic failure pattern of wrappers is underrepresented in training data.
Human judgment matters here. The ability to evaluate whether a product idea falls into the wrapper trap. The wisdom to distinguish sustainable AI value from temporary arbitrage. The experience to recognize patterns that AI synthesis doesn’t capture.
This is becoming a meta-skill: evaluating AI business opportunities using human pattern recognition that AI-synthesized advice lacks.
Automation-aware thinking means recognizing that AI-generated advice about AI businesses may be systematically optimistic about opportunities that historically fail.
The Honest Assessment
Let me be honest about what I’ve seen.
Most AI side hustles launched between 2023 and 2025 failed. Not because the builders were incompetent—many were skilled. Because the opportunity was misunderstood.
The opportunity wasn’t “build a UI around AI.” The opportunity was “build things that AI enables but can’t provide alone.”
The first approach led to wrappers. Wrappers died.
The second approach led to products with defensible value. Some of those survive and thrive.
The lesson isn’t “don’t build AI products.” The lesson is “don’t build products where AI is the only value.”
What Would I Build
If I were starting an AI-powered side hustle today, here’s my approach:
Start with the problem, not the technology. What problem do specific people have that AI could help solve? Not “what can AI do” but “what do people need.”
Add proprietary value from day one. Data I collect. Integrations I build. Workflows I understand. Something beyond the AI API call.
Target a domain I know deeply. Generic AI products compete with ChatGPT. Domain-specific products compete on domain expertise. I have domain expertise. ChatGPT doesn’t.
Design for the second month. What makes users stay? What would they lose by leaving? Build that from the start, not as an afterthought.
Accept smaller markets for defensibility. A product that serves 10,000 people deeply beats a product that serves 1,000,000 people shallowly and loses them after trial.
The Renewal Mindset
The wrapper trap ultimately comes down to mindset.
Wrapper builders thought about acquisition. How do we get users? Subscription businesses should think about retention. How do we keep users?
Acquisition is necessary but not sufficient. A product that acquires users and loses them is just expensive marketing. A product that retains users builds compounding value.
The renewal mindset asks different questions. Not “what sounds impressive at launch” but “what creates ongoing value.” Not “what gets people to sign up” but “what prevents people from canceling.”
Simon has demonstrated excellent retention principles by remaining on my lap for the past hour despite multiple attempts to relocate him. His value proposition—warm, purring presence—creates genuine ongoing benefit. He could teach retention strategy to most SaaS founders.
The Path Forward
The AI wrapper trap was a learning experience for the industry. Many people built the obvious product and discovered why it was obvious—and wrong.
The path forward is building less obvious products. Products where AI is a component, not the entirety. Products where value accumulates. Products where users can’t easily replicate the experience by prompting directly.
This is harder than wrapping an API. That’s why it’s more likely to work.
The easy opportunity was never the real opportunity. The real opportunity was always building things that are hard to replicate. AI makes some hard things easy. It doesn’t make business moats obsolete.
Build something people renew. That’s the test. Everything else is vanity metrics on the way to failure.
The Competitive Dynamics
Understanding why wrappers fail also requires understanding competitive dynamics in AI markets.
When you build a wrapper, you’re competing with several forces simultaneously.
The underlying model itself. ChatGPT, Claude, Gemini—they all have direct interfaces. Users can access them without your wrapper. And the interfaces keep improving.
Other wrappers. Hundreds of people had the same idea you did. They’re all building similar products. Feature comparison becomes impossible. Price competition becomes inevitable.
The user’s own capability. As users become more sophisticated with AI, they need wrappers less. Your market shrinks as your users learn.
Free alternatives. Open source projects, free tiers, community tools. Someone is always willing to provide your functionality for free to build reputation or portfolio.
These dynamics create a squeeze. Price pressure from competition. Value pressure from the underlying platform. Demand pressure from user sophistication. The wrapper is being crushed from all directions.
The Timing Illusion
Many wrapper builders believed they had a timing advantage. “We’re early. We’ll build brand while users are still confused about AI.”
This logic failed for a specific reason: the confusion window was shorter than expected.
AI literacy spread faster than anticipated. ChatGPT’s user interface improved rapidly. The window where users needed wrappers to access AI capabilities lasted months, not years.
Building a business on temporary user confusion is building on sand. The confusion resolves. The business dissolves.
The timing advantage for sustainable AI businesses is different. It’s not “users don’t understand AI yet.” It’s “this specific domain problem hasn’t been solved well yet.” Domain problems persist. General confusion doesn’t.
Case Studies in Wrapper Death
Without naming specific products—many founders are still recovering—let me describe patterns I observed.
The writing assistant wrapper. Took text input, sent it to GPT with “improve this writing” prompt, returned output. Launched to 50,000 signups. Month-two retention: 8%. Users discovered they could just ask ChatGPT directly.
The code completion wrapper. Added AI suggestions to code editors. Worked well initially. Then GitHub Copilot integrated deeply. Then the editors added native AI. The wrapper became redundant.
The research summary wrapper. Summarized documents using AI. Useful until users learned to paste documents into ChatGPT themselves. Value proposition evaporated.
The social media content wrapper. Generated posts from prompts. Users used it for a month, learned the prompts, then ran them directly. Zero reason to continue paying.
Each product solved a real problem temporarily. None solved a problem that remained difficult as users and platforms evolved.
Building Against the Grain
The successful AI products I’ve observed share a common characteristic: they’re harder to build than wrappers.
This is counterintuitive advice. Harder is supposed to be worse. But in AI products, harder is protective.
If your product is easy to build, someone else has already built it. If your product is hard to build, fewer competitors exist.
If your value can be replicated with a prompt, users will replicate it. If your value requires data, integration, or expertise that can’t be prompted, users need you.
Building against the grain means choosing the harder path deliberately. Not because suffering is virtuous. Because difficulty is defensive.
The Long-Term View
AI capabilities will continue improving. Models will get better. Interfaces will get simpler. User sophistication will increase.
This means the bar for AI-powered businesses keeps rising. What seemed like valuable differentiation in 2023 is commodity in 2027. What seems like differentiation today will be commodity in 2030.
Sustainable AI businesses must stay ahead of this curve. Not by running faster on the same track, but by building value that doesn’t erode as underlying capabilities improve.
Proprietary data doesn’t erode when models improve—it becomes more valuable as better models can extract more from it. Deep integrations don’t erode—they become stickier as users become more dependent. Domain expertise doesn’t erode—it compounds as understanding deepens.
The long-term view asks: “Will this value still matter when AI is 10x better?” Wrappers fail this test. Products with accumulated, non-replicable value pass it.
The Final Lesson
The AI wrapper trap taught an important lesson about business building: technology capabilities are not business moats.
Being able to access AI was never a differentiator. Everyone can access AI. The differentiator was always what you built around that access.
The products that survive are the ones that would have been valuable even without AI—but AI makes them dramatically better. The products that died were ones that had no value except the AI access they provided.
Build something that matters beyond the API call. Build something people would miss if it disappeared. Build something that earns renewal through accumulated value, not through user confusion.
That’s how you avoid the wrapper trap. That’s how you build something people actually renew.