Anyone who has pitched anything with even a whiff of AI in it over the last year has inevitably run into a moment like this: the founder is at the peak of their pitch when the partner leans back expressionless and mutters “So, you’re building a ChatGPT wrapper?”
This single phrase accounts for more killed momentum and discouraged founders than any other in recent history. The mere concept of a ChatGPT wrapper suggests that building on top of foundational models is derivative, lazy and inevitably doomed to fail.
But the idea that “wrappers” aren’t real businesses has always been a colossal misunderstanding, and it will generate plenty of regret for those who passed on early deals only to see the business model surge just a year later. Having money first doesn’t always mean having superior business sense, even when it comes with the power to make, or break a fledgling business.
The tides are shifting rapidly when it comes to how foundational models are being used, and with interest rates showing hints of easing and AI adoption hitting its second wave, no VC worth their salt will use “ChatGPT wrapper” as a slur again. Here’s why.
Why the ChatGPT wrapper insult never made sense
The best way to think about AI is not as a technology but as a tool. That framing is crucial to help us think more clearly about AI because calling it a technology feels inherently fuzzy and abstract.
A tool, on the other hand, is something concrete, and viewed from this perspective, it becomes clear that tools have two inherent features about them. First, they supercharge their users. A power drill drives nails straighter, a spreadsheet lets an analyst model financials once far beyond reach just like a language model now extends our reasoning to superhuman scale.
Second, most businesses should never even consider making their own tools. From this perspective, mocking someone for building on a GPT is a literal self-burn that reveals ignorance, not insight. It’s like laughing at carpenters for using Makita drills or MDs not building their own FMRI analysis models.
Scott Stevenson, co-founder of Spellbook, a legal AI startup that now serves more than 3,600 law firms and in-house attorneys puts it bluntly:
“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.”
To drive the point home, Spellbook itself has built a thriving business by doing exactly that. It started with GPT-3.5, now runs across Anthropic, Cohere, OpenAI, and GPT-5, and supplements them with proprietary models, memory techniques and RAG.
They call themselves an “electric bicycle for lawyers,” not replacing the rider, but giving them a massive speed boost, and they aren’t about to build the whole bicycles from scratch.
“Calling something a ChatGPT wrapper was never really an argument about the underlying tech, it was more a sign of how people sometimes struggle to disentangle the tool from the opportunity. They couldn’t see that the transformation wasn’t the model itself, but what people could build with it.”
The wrapper slur never made sense because businesses have always been built on foundational tools. No one calls Salesforce a “database wrapper” just like no one sneers at construction firms for using standardized nuts and bolts instead of smelting their own nails.
What matters is execution and the value it delivers. And yet, the wrapper-skeptics aren’t entirely off target with their fears.
But what if OpenAI comes for your business?
The deeper fear many VCs still harbor today is that the toolmaker itself, be it OpenAI, Microsoft, or Google, would eat your lunch. And it’s not at all an unfounded one, given how the “kill zone” around hyperscalers is a very real phenomenon.
And here’s where those who once dismissed the “wrapper” weren’t entirely wrong.
A new UI slapped onto an API that just about anyone could call was always fragile. If all you offered was a different button to press on the same underlying service, you were always going to be subsumed. That’s not the efficient way to build, and frankly, those businesses deserve to be swallowed. Value matters, and simply rerouting and gatekeeping access to a tool you didn’t isn’t valuable in itself.
The trouble was that this nuance got lost in the early hype. Too many AI startups billed themselves as transformative when all they had was a slightly different on-ramp to ChatGPT.
There’s nothing wrong with being a wrapper or even training your own model, but transparency about it matters. If all you’ve built is a quicker way to access GPT and you pitch it as something more, you’re not just fooling the market, you’re fooling yourself.
That’s the real danger here is self-delusion.
The worst trap a founder can fall into is convincing themselves they’ve built a company when what they’ve actually created is a shortcut. A business that positions itself as a platform when it’s just a thin layer over someone else’s model is setting itself up for disaster. At some point, narrative has to match reality and where it is going, or the whole thing collapses.
It’s the same lesson from the App Store’s early days, the veterans of which now not to bet a career on flashlight apps.
This is where the most resilient builders stand out and persist to build the second wave of AI-native companies. They accept the premise that models are tools, and focus instead on where the moat really lies: proprietary data, fine-tuning, user trust, and workflow mastery.
Which is why Stevenson stresses: “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 message the second wave of AI-native companies is delivering is clear. Thin wrappers were always destined to fade, but companies that combine models with proprietary muscle, integration, and transparency aren’t wrappers at all, they’re the ones creating the real value layer in the AI economy.
The second wave of AI-native apps is wrapping, and then some
The shift now underway is from novelty to necessity. The first wave was all about showing “look, it’s AI.” The second wave is about solving real problems with it.
Joseph Semrai, founder of Context, an AI-native office suite that just raised $11M, frames it this way:
“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.”
That pragmatism defines the new class of founders. They know when to build, when to buy, and when to plug in an API. And they’re building not just features, but entirely new paradigms. Context, for instance, isn’t trying to bolt AI onto Excel or Google Docs with an API call. Instead, it’s aiming for nothing less than reimagining the office suite from the ground up, with foundational models fueling it all.
As Semrai told me: “This has really only become feasible in the last months. The utility is here and now, the tool-calling abilities are essential. They allow agents to work for hours at a time, and that’s the breakthrough that makes something like an AI-native office suite possible.”
Given how quickly foundational models have advanced, the second wave of AI-native companies also deliver an important lesson in the pace of adoption. As Semrai put it:
“The tech is advancing so quickly that the pace at which people can deploy and change their tools has become the bottleneck. The models aren’t the constraint; adoption and process are.”
That’s a far cry from the first wave of AI apps that were little more than shiny demos, many of which were solutions in search of a problem. This new wave, on the other hand, is equally ambitious yet much more deeply focused on workflow integration.
And some are taking the concept of wrapping a GPT to places where the models begin folding on themselves. Pangram, Max Spero’s AI detection company, is fighting fire with fire by using language models to catch AI-generated text with industry-leading accuracy. If the first wave was about creating, the second wave is just as much about discerning.
Spero is clear-eyed about the stakes:
“AI has raised the minimum bar on acceptable quality. If you’re just spinning out rehashed internet text, nobody wants it. The real opportunity is in combining fine-tuning, proprietary data, and foundational firepower to solve problems people actually care about.”
But to do that, you need to understand what AI can’t do.
“This is one of the few remaining tasks where AI can’t tell if it’s AI unless it has been trained to do so. So we had to train our own models that don’t just predict the next token, but evaluate whether an entire piece of text is human or machine.”
And as Spero warns, the need for the edge delivered by understanding model limitations, and how to use them to your advantage, will only grow:
“Mode collapse means AI tends to say the most likely thing—we lose the long-tail of originality. That’s why building systems to spot, verify, and enhance human originality is so critical. We’re fighting fire with augmented fire.”
That is not a story told by founders building simple wrappers. This is a story of AI-native companies that are building moats around understanding their tools better, and learning to deploy them in search of deeper and deeper sources of value.
May You Be the best ChatGPT wrapper you can be
Today, dismissing a startup simply because it uses GPT or Claude should be grounds for losing your accredited investor license. Homebrewing foundational models may sound sexy on a pitch deck, but it’s as pointless as CNC machining your own screws.
Foundational models have proven their versatility across law, healthcare, enterprise productivity, and even AI detection. They are infrastructure, like power grids or cloud computing, and the value lies in how you use them.
Steve Lucas, CEO of Boomi, cautions that not all use cases are created equal.
“AI itself won’t win. Success comes from integrating it into the way your business actually operates. You have to know what to deploy and with what. Deterministic models belong in payroll and compliance, because even one probabilistic answer there can be a disaster waiting to happen. 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.”
That hybrid thinking is what separates the durable businesses from the thin wrappers. Lucas is blunt about the cost of ignoring this discipline: “Too many point solutions, too many wrappers on the wrong problems, and you get cognitive overload. Humans already spend 30–40% of their time just moving data around. AI should reduce that burden, not add another layer of noise.”
The implication is clear, the underlying tech, whether a wrapper or something more, isn’t enough. What matters is building the connective tissue around what makes the technology additive, not overwhelming. As Lucas told me: “Irrespective of your tech stack, leaders today have to act as integrator-in-chief. If you don’t understand your data and your knowledge processes, no wrapper in the world will save you.”
The real open question is what happens when the foundation providers themselves pivot. If OpenAI or Anthropic ever decide to become service companies, competing directly with the applications built on top of them, the dynamic will shift. But the sheer difficulty and cost of shipping ever-bigger models may well keep them focused on the core, at least for the time being.
Until things change and the kill-zone shifts again, the winning play is clearly to something more than just a wrapper. Be the best possible wrapper you can by layering proprietary value, workflow mastery, and user trust on top of the most powerful tools available, no matter who made them and where.
By the time you’ve finished reading this, the old insult has boomeranged. The fools weren’t the founders building on GPT. The fools were the ones who thought that was a weakness.