Wrapping Your Head Around AI Wrappers
Why some wrappers build billion-dollar businesses while others disappear overnight.
“That’s just an AI wrapper.”
The put‑down is now familiar for anyone developing something new using Artificial Intelligence.
The push-back is just as familiar.
“Everything is a wrapper. OpenAI is a wrapper around Nvidia and Azure. Netflix is a wrapper around AWS. Salesforce is an Oracle database wrapper valued at $320 billion.”, says Perplexity CEO Aravind Srinivas1.
For those not familiar with the term “AI Wrapper”, here’s a good definition2.
It is a dismissive term that refers to a lightweight application or service that uses existing AI models or APIs to provide specific functionality, typically with minimal effort or complexity involved in its creation. A popular example of an AI wrapper are apps that enable users to “chat” with a PDF. This type of AI application allows users to upload a PDF document, such as a research paper, and interact with an AI model to quickly analyze and obtain answers about the specific content. In the early days of ChatGPT, uploading documents as part of the prompt or creating a custom GPT was not possible, so these apps became very popular, very fast.
In my view, this AI wrapper debate misses a larger point. Wrappers are not all the same. Thin tricks enjoy a brief run and last only until big platforms bundle them into their suites. But products that live where users already work, write back to a proprietary system of record, and/or can make use of proprietary data can endure. The wrapper label is a distraction from what I think actually matters: (1) Is it a feature or a product, and (2) How big is the market segment.
Feature Or Product
Begin with the earlier example of a wrapper that lets you chat with a PDF. Such a tool solves one narrow problem - answering questions about a document. It does not create new documents or edit existing ones. It typically does not offer any unique workflow, capture any unique data, or learn from user behavior. It is a means to an end; a capability rather than an end-to-end solution. As a result, this kind of feature belongs inside a document viewer or editor, or in the flagship applications of model providers. So when the models themselves (OpenAI/ChatGPT, Anthropic/Claude, Google/Gemini) bundle this feature natively, the standalone tool becomes redundant. This is classic feature behavior: easy to copy, no end-to-end job, no moat or long-term defensibility.
One caveat though; even those that are features can be an interesting indie businesses that make money until the platforms build it into their apps3.
Too Big To Ignore
Some wrappers are genuine products but live in market segments so large that model builders and big tech platforms cannot ignore them. Two vectors of competition come into play: (1) model access, and (2) distribution.
Model Access
Coding assistants illustrate both. Tools like Cursor turned a wrapper into a development environment that reads the repo, edits files, writes code, reverts changes, runs agents, and reimagines the developer experience for the AI-era. The market justifies the attention. Software developers represent roughly 30% of the workforce at the world’s five largest market cap companies, all of which are technology firms6. Development tools that boost productivity by even modest percentages unlock billions in value. That makes this segment a prime target for both model builders and incumbents that already own distribution channels.
But Cursor and other such tools depend almost entirely on accessing Anthropic, OpenAI and Gemini models. Developer forums are filled with complaints about rate limits from paying subscribers. In my own experiences, I exhausted my Claude credits in Cursor mid-project and despite preferring Cursor’s user interface and design, I migrated to Claude Code (and pay ten times more to avoid rate limits). The interface is good, but model access proved decisive.
This model builder competition extends to every category that OpenAI Applications CEO Fidji Simo flagged as strategic (Knowledge/Tutoring, Health, Creative Expression, and Shopping) as well as other large market segments such as Writing Assistants, Legal Assistants, etc.
Distribution
Distribution poses the second threat. Even where model builders stay out, startups face a different race: can they build a user base faster than incumbents with existing products and distribution can add AI features? This is the classic Microsoft Teams vs. Slack Dynamic7. The challenge is in establishing a loyal customer base before Microsoft embeds Copilot in Excel/PowerPoint, or Google weaves Gemini into Workspace, or Adobe integrates AI across its creative suite. A standalone AI wrapper for spreadsheets or presentations must overcome not just feature parity but bundling/distribution advantages and switching costs.
This distribution competition from incumbents also holds in healthcare and law. These are large markets but regulatory friction and control of systems of record favor established players such as Epic in healthcare. For e.g. A clinical note generator that cannot write to the Electronic Health Record (EHR) will likely come up against Epic’s distribution advantages sooner or later.
Two caveats here: (1) First, speed to market can create exit options even without long-term defensibility; tools like Cursor may lack control over its core dependency (model access), but rapid growth make them attractive targets for model builders seeking instant market presence. (2) Second, superior execution occasionally beats structural advantage; Midjourney’s product quality convinced Meta to use it despite Meta’s substantially larger budget and distribution power.
The opportunity remains large8, but competition (and/or acquisition) can come knocking.
Cursor went from zero to $100 million in recurring revenue in 18 months, and became the subject of recurring OpenAI acquisition rumors.
Windsurf, another coding assistant, received a $2.4B
acquisitionlicensing deal from Google.Gamma reached $50 million in revenue in about a year.
Lovable hit $50 million in revenue in just six months.
Galileo AI acquired by Google for an undisclosed amount.
The Entrepreneur’s Opportunity
Not every market gap attracts model builders or big tech. A long tail of jobs exists that are too small for venture scale but large enough to support multimillion-dollar businesses. These niches suit frugal founders with disciplined scope and lean operations.
Consider those Manifestation or Horoscopes or Dream Interpreter AI apps. A dream interpreter that lets users record dreams each morning, generates AI videos based on them, maintains some kind of dream journal, and surfaces patterns over time solves a complete job. Yes, users could describe dreams to ChatGPT and it even stores history/memory, but a dedicated app can structure the dream capture with specific fields (recurring people, places, things, themes etc.) and integrate with sleep tracking data in ways a general chatbot likely cannot. Such a niche is small enough to avoid model attention but large enough to sustain a profitable indie business.
Models Versus Incumbents
While the previous categories frame opportunities for new ventures, incumbents face their own strategic choices in the wrapper debate when model builders arrive. Those that survive model builder competition share two characteristics.
First, they own the outcome even when they don’t own the model. Applications already embedded in user workflows (Gmail/Calendar, Sheets, EHR/EMR, Figma) require no new habit formation, and building these platforms from scratch is much harder than adding AI capability to existing ones. When these applications ship actions directly into a proprietary system of record (managing the calendar, filing the claim, creating the purchase order, and so on), “done” happens inside the incumbent’s environment. AI becomes another input to an existing workflow rather than a replacement for it.
Second, successful incumbents build proprietary data from customer usage. Corrections, edge cases, and approvals become training data that refines the product over time, that a frontier model will not have access to. Cursor, though not an incumbent and despite its dependence on external models, plans to compete by capturing developer behavior patterns as CEO Michael Truell notes in his Stratechery interview:
Ben: Is that a real sustainable advantage for you going forward, where you can really dominate the space because you have the usage data, it’s not just calling out to an LLM, that got you started, but now you’re training your own models based on people using Cursor. You started out by having the whole context of the code, which is the first thing you need to do to even accomplish this, but now you have your own data to train on.
Michael: Yeah, I think it’s a big advantage, and I think these dynamics of high ceiling, you can kind of pick between products and then this kind of third dynamic of distribution then gets your data, which then helps you make the product better. I think all three of those things were shared by search at the end of the 90s and early 2000s, and so in many ways I think that actually, the competitive dynamics of our market mirror search more than normal enterprise software markets.
Unwrapping the Wrappers
Both critics and defenders of AI wrappers have a point, and both miss something crucial. The critics are right that some wrappers lack defensibility and will disappear when platforms absorb their features. The defenders are right that every successful software company wraps something.
But the real insight lies between these positions. Even if a new application starts as a wrapper, it can endure if it embeds itself in existing workflows, writes to proprietary systems of record, or builds proprietary data and learns from usage. These are the same traits that separate lasting products from fleeting features.
Perplexity AI CEO, Aravind Srinivas pushing back on criticism about the business potential of Perplexity:
https://medium.com/@alvaro_72265/the-misunderstood-ai-wrapper-opportunity-afabb3c74f31
https://ai.plainenglish.io/wrappers-win-why-your-ai-startup-doesnt-need-to-reinvent-the-wheel-6a6d59d23a9a
https://aijourn.com/how-ai-wrappers-are-creating-multi-million-dollar-businesses/
https://growthpartners.online/stories/how-jenni-ai-went-from-0-to-333k-mrr
Microsoft bundled Teams into Office 365 subscriptions at no extra cost, using its dominant enterprise distribution to surpass Slack’s paid standalone product within three years despite Slack’s earlier launch and product innovation. https://venturebeat.com/ai/microsoft-teams-has-13-million-daily-active-users-beating-slack
https://a16z.com/revenue-benchmarks-ai-apps/