GenAI wrappers are quietly printing millions. Stop theorizing & start building—there’s still time to make a fortune.

When GPT-3.5 and ChatGPT dropped in December 2022, it opened floodgates for new opportunities in text, voice, image, video, and beyond.

Two tribes emerged: those who started building—the makers—and those who objected that building on top of AI models was not defensible—the thinkers.

Spoiler: tons of makers got rich.

It’s a new gold rush.

Opportunities are aplenty. Every corner of the web & the world is ripe for the taking.

And it’s not too late to join the wave. It’s even better than the 2010s SaaS era:

Many of you are skeptics, and rightfully so, so let’s start with a list of successful GenAI wrappers from the GPT-era [1] ⬇️

Examples of success

Bootstrapped

VC-backed:

Other Hot Segments:

These examples show that success is possible—but how can you replicate it? It starts with understanding the common patterns among these winning products.

How did they do it?

These makers built something people wanted:

Does it mean it’s easy and trivial? No. Building profitable software is still tough. But it’s never been easier.

Take RizzGPT. It seems trivial, but it works because it’s convenient. People won’t realize they can use ChatGPT for dating tips, but an app spoon-feeding the solution? Game on.

Vatsal Sanghvi’s sums it up:

Users don’t care whether you are building a wrapper or not, as long you solve a problem for them — Users will use your product

Of course, not everyone is convinced. As the success of these wrappers has grown, so have the criticisms. Let’s tackle the most common objections head-on.

Common Objections & Rebuttals

Objection: “There’s no moat”

Sure, GenAI tech alone isn’t defensible, but moats come later. Use the wow-effect as a lead magnet and monetize fast. Then expand your product with regular features to lock in users.

To see how this objection plays out in reality, consider these case studies of companies that adapted and thrived, despite the perceived challenges:

The playbook to build a successful wrapper is still the same, be it in 2016 with Elasticsearch, or in 2024 with GenAI models: in the early days, speed beats strategy.

But the “no moat” crowd isn’t entirely wrong either—AI wrappers face fierce competition, especially as more entrepreneurs spot the opportunity. The secret is understanding that you don’t need a moat to start, but you’ll need one to keep growing.

So you can stop after the first few millions and sell your business—but if you’re in for the long-run, to survive the next phase, you’ll need to think about defensibility. In B2C, this often means building a brand. In B2B, it’s about embedding into workflows, as explained by NFX.

Objection: “Who would use this? Anyone can just build their own RAG / use Langchain / use open source models from Hugging Face”

Reality check: most people aren’t techies, and even fewer will use open-source models from Hugging Face. Only 43% of 18-29-year-olds had used ChatGPT by Feb 2024 (source).

Yes, someone could copy-paste their Bumble chat into ChatGPT, but they won’t. Convenience wins, and techies underestimate how lazy or uncreative people are.

Objection: “GPT-5 will kill you”

Sure, it might. But as a wrapper, you’re adaptable. Swap models as better ones emerge—Claude 3.5 Sonnet (new), GPT-5, whatever. Let the AI giants play war games while you focus on serving users.

Companies that got crushed by ChatGPT (like Jasper) built basic wrappers on GPT-3. When ChatGPT (3.5) was released, they got steamrolled — to use Sam Altman’s lingo — because they weren’t adding value. Lesson: Make your product something that improves as foundational models improve, not a product that dreads the next OpenAI release.

Objection: “But Kamil most wrappers fail and building a successful one is very hard — you have a survivorship bias!”

Success is still hard, but building profitable software has never been easier:

But yes, failure is a likely option. Even the best indie hackers like Pieter Levels fail most of their ventures, with a 4/70 success rate over 11 years.

Verticalized GenAI Wrappers are at the same time:

I loved this Twitter thread that explained why more and more Gen-Z founders prefer to bootstrap AI wrappers instead of looking for VC money:

Objection: “All the wrapper ideas have already been done — now it’s only red oceans full of sharks”

I will give it to you, among the list of winners that I shared, a lot of them had a first-mover advantage. I wouldn’t recommend you to build a RizzGPT copycat today.

But you could be the next first-mover.

Because new technologies are released every other week.

Anthropic released its “Computer Use” API 10 days ago (Oct. 22, 2024), it uses computers the way people do—by looking at a screen, moving a cursor, clicking, and typing text.

OpenAI released a public real-time API for its advanced voice mode 30 days ago.

So many use cases that were impossible before are now solvable through a simple wrapper.

And so many more get unlocked every passing day.

Even without new technologies, there is an infinite number of industries, use cases, niches, that are still waiting for their GenAI savior app to come fix their problems.

Problems are everywhere. Hidden in plain sight. Waiting for you to solve them.

I was yet reminded of this underrated truth this week, while hanging out at a friend’s rooftop party in San Francisco.

I met with Alex Andrei, who’s co-founding Claritycare AI. Their founding story was quite simple: they were in the middle of an EntrepreneurFirst batch (startup incubator), looking for problems to solve in the healthcare space.

At the time, Andrei realized that his wife Emelia, a “back office pharmacist” for a health insurance company, was spending a lot of time manually reviewing customer’s requests asking the insurance to pay for their treatment.

Alex, being a technologist, had the idea to use an LLM to solve this problem. It worked like magic. This is how Alex & his co-founder decided to double down on this problem with his co-founder.

I loved this story because it shows that niche problems — with huge market sizes nonetheless :) — are everywhere.

Hidden in plain sight.

Waiting for the right person to discover and solve them.

This could be you. What are you waiting for?

I’m feeling generous, so I’m sharing lists of problems at the end of the article ⬇️

How to Do It Yourself (Even If You Don’t Code)

If I had to start an AI Wrapper from scratch, here’s how I’d do it:

  1. Find a Problem in a Paying Niche: Ideally one I know well. More on that below.

  2. Build It With AI Tools: You can literally build a full app (web/mobile) from scratch in a matter of hours with very little knowledge of code:

    1. Build with AI assistants:

      1. Full-stack builders (front, back, db, deployment): GPTEngineer, Bolt, Agent by Replit
      2. Front-end builder: V0 by Vercel
      3. Code assistants: Cursor’s Composer
      4. Check out this video by Greg Isenberg on how to use these tools

    2. Depending on the type of content I’m trying to generate, I’d use the APIs of:

  3. Create a Tailored Interface: I’d make it relevant to the user’s workflow.

  4. Monetize Day 1

  5. Distribute: I’d distribute the product on the relevant online communities (subreddits, discord servers, facebook groups) and, if B2C, heavily through Tiktok (UGC content creators, influencers deals & paid ads)

    1. Read this amazing article by Startup Spells on how the best GenAI wrapper B2C founders do their distribution (Arib Khan, co-founder of Musicfy.lol and Crayo.ai)

  6. Exit or Defend:

    1. If I want to play the short-term game, I would exit while revenue numbers look good
    2. If I feel like fighting against the inevitable copycats, I’d consider raising funds & investing in an engineering department to build workflow features and increase defensibility

While each of these points deserves a full article, I won’t pretend I have a magical recipe 😅. If you want to dive deeper into these topics, check out the further readings at the end of my article. I also strongly encourage you to read & watch content by the founders of the successful wrappers I shared. They’re very generous with their advice and often build in public.

List of problems

The best problem to pick is one you’re facing yourself — scratch your own itch. Otherwise, browse some of these resources for ideas:

Elon Musk dropped out in the 90s to launch a startup because the Internet wave was too big to ignore.

Today, GenAI is undeniable—will you ignore it?

THE END—go build now

About me

Hey guys, thanks for reading through my rants.

I’m Kamil. I’ve been building in AI for 5 years. I’m a 2x founder and currently Product Manager in a GenAI company: Poolday.ai lets mobile app marketers generate performing video ads in seconds.

I live in San Francisco & my favorite thing is to meet curious, ambitious, and contagiously enthusiastic people.

PS: my next article will be on how to mitigate non-deterministic outputs to build successful GenAI products — ping me if you’re interested about this topic!

Thanks to friends for reading drafts of this

Notes

[1] These figures are self-reported by founders. True or not, they’re directionally accurate.

Further Readings – Sources & Interesting stuff