Every week, someone pitches me an AI startup that’s basically ChatGPT with a nicer interface.

“We’re building the [X] for [Y industry],” they explain. Their differentiation is prompts, UI, and maybe some light integrations.

Most of these companies won’t exist in two years. Here’s why—and what the survivors do differently.

The Wrapper Problem

An “AI wrapper” is a product built primarily on top of large language models (LLMs) like GPT-4, Claude, or Gemini. The startup adds interface, prompts, and sometimes integrations, but the core intelligence comes from the underlying model.

The business model problem is straightforward:

You’re reselling someone else’s commodity. API costs are your biggest expense. Margins depend on OpenAI, Anthropic, or Google’s pricing decisions—which you don’t control.

Differentiation is thin. If your value is prompts plus UI, that’s replicable in weeks. Your competitor (or the LLM provider themselves) can copy your best ideas.

Models keep improving. Features you built as differentiation become standard capabilities in the next model release. Your moat fills in automatically.

The platforms are coming. OpenAI’s GPTs, Anthropic’s Claude projects, Google’s Gems—the model providers are building wrapper functionality directly.

What Dying Looks Like

I’ve watched this pattern repeatedly:

Month 1-6: Rapid growth. Early adopters excited about domain-specific AI tool. Good press coverage. Seed round closes easily.

Month 7-12: Growth slows. Larger competitors launch similar features. Customer acquisition costs rise. API costs don’t decrease as hoped.

Month 13-18: Retention problems emerge. Customers trying direct model access or competitor products. Revenue plateaus.

Month 19-24: Runway concerns. Series A conversations stall. Pivot or shutdown discussions begin.

Not every AI wrapper follows this path. But enough do that the pattern is predictable.

What Survivors Do Differently

The AI startups with defensible positions share certain characteristics:

They own data the models don’t have

Canva’s AI features work because they’re trained on decades of design data. Generic LLMs can discuss design principles; Canva can actually apply them with brand-specific context.

If your startup has proprietary data that improves model outputs—and that data is expensive or time-consuming to replicate—you have something.

They have workflow lock-in

Products that integrate deeply into business processes are sticky even if the AI component is replicable. When switching costs include retraining teams, rebuilding integrations, and migrating data, customers stay despite alternatives.

CRM-embedded AI is harder to replace than standalone AI assistants, even if the underlying intelligence is identical.

They solve problems models can’t

LLMs are general-purpose. Specific domains need:

• Real-time data access (models have knowledge cutoffs)
• System integrations (models can’t execute actions)
• Compliance requirements (models don’t understand your regulatory context)
• Human-in-the-loop workflows (models alone aren’t sufficient)

Startups that address these gaps add genuine value beyond the model layer.

They compete on speed and reliability

Enterprise buyers often care more about reliability, security, and support than raw capability. A wrapper with SOC 2 compliance, SLAs, and dedicated support may win over direct API access.

This is less sexy than technical differentiation but practically valuable.

The Honest Conversation

If you’re building an AI wrapper, ask yourself:

What happens when the model providers add your features? OpenAI adding memory, tool use, and customisation eliminates many wrapper value propositions.

Could a motivated competitor replicate your product in 90 days? If yes, VC funding probably isn’t appropriate. Maybe it’s a good bootstrapped business, maybe it’s not a business at all.

What’s your unfair advantage? This old startup question matters more for wrappers. Distribution, data, domain expertise, relationships—something needs to create distance from fast followers.

Are you solving a real problem or enabling a novelty? Some AI tools see strong initial interest that fades as the novelty wears off. “Playing with AI” isn’t the same as “solving workflow problems with AI.”

What to Do Instead

If the wrapper approach isn’t defensible, consider:

Go vertical, go deep

Rather than general-purpose tools, build for specific industries with domain expertise competitors can’t easily acquire. Healthcare, legal, finance—sectors where understanding context matters as much as the AI capability.

Build the picks and shovels

Infrastructure for AI applications—monitoring, testing, deployment, security—has better margins and higher switching costs than end-user applications.

Combine AI with non-AI differentiation

AI might be one component of a larger platform. The AI gets people in the door; other features create retention.

Consider services over pure product

Some founders are better suited to consulting and implementation than product building. AI consultants Brisbane firms can build sustainable practices helping businesses implement AI—without the pressure of scaling a SaaS product.

The Uncomfortable Reality

Most AI startups in 2024-2025 were founded on enthusiasm for the technology rather than clear understanding of business defensibility. That’s not criticism—it’s how every tech wave works.

But as the market matures, the companies that survive will be those that found genuine moats, not those that moved fastest to wrap existing models.

If your wrapper startup is struggling, the honest question is whether there’s a path to defensibility—or whether it’s time to pivot toward something more sustainable.

Some wrappers will survive and thrive. Most won’t. The difference is rarely the quality of the team or the product. It’s whether the structural economics ever made sense.