Beyond The Wrapper How AI Startups Create Value

Tell me I'm just a ChatGPT wrapper one more time. - getty

For the past year, AI founders have dreaded a single question from venture capitalists: “So, you’re building a ChatGPT wrapper?” This simple phrase has stalled momentum and discouraged countless entrepreneurs, creating the impression that building on foundational models is lazy, unoriginal, and destined for failure.

However, this perspective has always been a significant misunderstanding of how technology and business work. The tides are now shifting. As AI adoption enters its second wave, the dismissive “wrapper” label is becoming a sign of ignorance, not insight. Those who passed on early deals may soon regret it, as the true value of these companies becomes undeniable.

Why The Wrapper Insult Was Always Flawed

The most effective way to understand AI is to see it as a tool, not an abstract technology. Like a power drill that enhances a carpenter's ability or a spreadsheet that extends an analyst's reach, a large language model extends human reasoning to a superhuman scale. From this perspective, criticizing a company for using a foundational model is like mocking a doctor for not building their own MRI machine.

Most businesses should not be making their own tools. Scott Stevenson, co-founder of Spellbook, a legal AI startup serving over 3,600 law firms, puts it this way: “The GPT wrapper discussion was always a misunderstanding. Software has always wrapped something. Salesforce is a database wrapper. Storage solutions are often Amazon S3 wrappers. Great software is built by using useful tools, and there is a lot of nuance that goes into building your layer on top.”

Spellbook is a prime example. The company started on GPT-3.5 and now leverages models from Anthropic, Cohere, and OpenAI, supplementing them with proprietary techniques. They position themselves as an “electric bicycle for lawyers,” providing a massive speed boost without building the entire bicycle from scratch. The criticism missed the point: the transformation isn't the model itself, but what innovators can build with it.

The Real Fear What If The Platform Competes

The deeper anxiety behind the wrapper accusation is the fear that the toolmaker—OpenAI, Microsoft, or Google—will eventually build a competing product and eliminate the smaller company. This is a legitimate concern, especially for businesses that are nothing more than a thin layer over an API.

If a startup's only value proposition is a slightly different user interface for the same underlying service, it is fragile and likely to be absorbed. The problem arises when founders delude themselves into thinking they have built a moat when all they have is a shortcut. This lesson was learned in the early days of the App Store, where simple flashlight apps quickly became obsolete.

However, the most resilient founders in the second wave of AI-native companies understand this. They accept that models are tools and focus on building their competitive advantage where it truly counts: through proprietary data, model fine-tuning, user trust, and deep workflow mastery.

As Stevenson notes, “Most of the time you don’t need to train a new model. What actually matters is adoption and driving value for your partners. If you do that, nobody cares whether you’re training from scratch or building on top.”

The Second Wave Beyond Simple Wrappers

The AI landscape is moving from novelty to necessity. The first wave was about demonstrating AI's capabilities; the second is about solving real-world problems. Joseph Semrai, founder of Context, an AI-native office suite, explains, “The biggest issue isn’t the model, it’s understanding workflows. Enterprises don’t care if it’s GPT-5 or Claude under the hood. They care that your product distills a mess of tools into something that actually works for them.”

This new class of founders is pragmatic, knowing when to build, buy, or use an API. Context isn't just bolting AI onto existing software; it's reimagining the entire office suite with AI at its core. This has only recently become possible due to advancements in model capabilities, which now allow AI agents to work on complex tasks for extended periods.

This second wave also highlights a new bottleneck: not the technology, but its adoption. The models are advancing faster than organizations can change their processes to integrate them.

Some companies are even using AI to solve problems created by AI. Pangram, an AI detection company, uses language models to identify AI-generated text. Founder Max Spero says, “AI has raised the minimum bar on acceptable quality... The real opportunity is in combining fine-tuning, proprietary data, and foundational firepower to solve problems people actually care about.” Pangram had to train its own specialized models because general-purpose AI can't reliably detect its own kind, demonstrating a deep understanding of the tool's limitations.

The New Playbook For Building On AI

Today, dismissing a startup just for using a third-party foundational model is like criticizing a construction firm for not forging its own nails. Foundational models are infrastructure, like the cloud or the power grid. The value is created in how they are used.

Steve Lucas, CEO of Boomi, emphasizes that success comes from intelligent integration. “You have to know what to deploy and with what. Deterministic models belong in payroll and compliance... Non-deterministic models, by contrast, can be transformative in creativity, research, and problem solving. The play is hybrid—deploying the right model for the right job.”

Businesses that simply add another layer of noise will fail. The goal is to build the connective tissue that makes AI an additive force, reducing cognitive overload rather than increasing it. The real question is what happens if foundation model providers pivot to compete with the apps built on their platforms. For now, the cost and difficulty of building these massive models keep them focused on their core infrastructure role.

Until that changes, the winning strategy is clear: be the best possible wrapper. Create durable value by layering proprietary data, workflow mastery, and user trust on top of the most powerful tools available. The fools were never the founders building on top of GPT; they were the investors who failed to see the opportunity.