In short5 min read
- The founder reports getting paying users within two weeks of launch.
- LLMs like AI chatbots were his major source of installs, not social media.
- He is relying on organic YouTube, TikTok, and Instagram content for now.
- Creator partnerships proved hard, though he admits his outreach volume was low.
Most indie apps die quietly in the first month. Nobody downloads them, and nobody pays. So when a solo founder posted on r/SaaS that his soccer app had paying users in week two, people paid attention. The bigger surprise was where those users came from: large language models.
By the end of this article, you’ll understand what this founder reported, why AI assistants are becoming a real discovery channel for small apps, and a concrete checklist to position your own app or micro-SaaS to get found the same way.
In this article
What the Founder Actually Reported
The post is short, so let’s be precise about what’s in it and what isn’t. The founder, posting as u/DyingBeast, shared a screenshot and wrote that it was “actually insane” that anyone was paying for his app. He said he “never thought” he’d get there.
Here’s what he shared:
- Timing: This was week two of the soccer app being live.
- Monetization: People are paying for it. He did not share revenue, pricing, or user counts in the text.
- Top install source: “LLMs have been the major source of installs,” which he called the most surprising part.
- Marketing: He’s producing organic content on YouTube, TikTok, and Instagram.
- Plan: Stick with organic content until he gets “one or two viral posts.”
- Struggle: Getting content creators to work with him has been “surprisingly hard,” though he admits his outreach has been “very low so far.”
That’s the whole data set. There are no revenue numbers to chase here, and you should be skeptical of anyone who turns this into a “$X in two weeks” story. The value is in the channel insight.
Why LLM-Driven Installs Matter Right Now
When the founder says LLMs drove installs, the most likely reading is that people asked an AI assistant something like “what’s a good app for soccer training” or “best app to track my soccer stats,” and the assistant recommended his app. He didn’t say which models or what prompts, so treat the mechanism as an informed interpretation, not a confirmed fact.
Either way, it points to a shift worth taking seriously. Search used to mean Google and the App Store. Now a growing slice of people ask a chatbot first. For a brand-new app with zero reviews and zero domain authority, that’s a potential back door.
The window feels open because this channel is still underused. Most indie founders are still fighting over App Store keywords and paid ads. Few are deliberately shaping how AI assistants describe their product. A founder who was surprised to get LLM traffic is a signal that the channel can work even without a deliberate strategy.
Where the Money Comes From
The business model here is simple: a consumer app in a passionate niche (soccer) that charges users directly. The post doesn’t say whether it’s a subscription or one-time purchase, so check your own niche’s norms before copying a price model.
What makes this kind of app monetizable:
- A passionate audience. Soccer players, parents, and coaches spend money on gear, training, and tools.
- A specific problem. Niche apps are easier for AI assistants to recommend because the query is specific.
- Low acquisition cost. Organic content and LLM recommendations cost time, not ad spend.
Channel Comparison
| Channel | Cost | Speed to results | Founder’s experience |
|---|---|---|---|
| LLM recommendations | Free (time to optimize) | Unpredictable | Major source of installs |
| Organic YouTube/TikTok/Instagram | Free (time to create) | Slow until a post hits | Ongoing, waiting for a viral post |
| Creator partnerships | Varies | Fast if it lands | Hard so far, low outreach volume |
| Paid ads | Paid | Fast | Not mentioned |
Monetization Difficulty: Medium
Building and shipping an app is real work, even with AI coding tools. Getting the first paying user is often the hardest milestone, and this founder cleared it early. But results like this depend heavily on execution: your niche, your product quality, and whether AI assistants happen to surface you.
The LLM channel is also hard to control. You can’t buy placement, and recommendations can change when models update. Treat it as a bonus channel you nurture, not the only pillar of your business.
The Creator Outreach Problem (and an Honest Fix)
The founder found it hard to get creators on board but was candid that his outreach was minimal. That’s the most fixable problem in the post. Creator outreach is a numbers game, and low volume almost always produces low results.
Realistic adjustments if you’re replicating this today:
- Target micro-creators. Smaller niche accounts are often more responsive than big names.
- Lead with value. Offer free lifetime access or a revenue share instead of asking for a favor.
- Personalize. Reference a specific video they made and explain why your app fits their audience.
- Track it. Keep a simple spreadsheet so you know your actual reply rate instead of guessing.
What to Copy and What to Skip
Copy:
- Picking a niche with an emotionally invested audience.
- Shipping fast and charging early. Payment is the real validation.
- Posting consistently on short-form video platforms.
- Paying attention to unexpected traffic sources and doubling down.
Skip or adjust:
- Waiting for virality. A viral post is a lottery ticket, not a plan. Keep posting regardless.
- Low-volume outreach. If creators matter to your plan, contact enough of them to get a real signal.
- Relying on luck for LLM visibility. You can make deliberate moves to improve your odds.
How to Do It: Your Step-by-Step Checklist
Use this to set up an app or micro-SaaS that can be discovered by AI assistants and grown with organic content.
- Pick a narrow niche with passionate users. Write down the exact questions they’d ask a chatbot, such as “best app for [specific task].”
- Test the LLM landscape. Ask several AI assistants those questions. Note which apps get recommended and why they’re described the way they are.
- Write a clear, plain-language product description. State who it’s for, what problem it solves, and what makes it different. Use it consistently on your website, app store listing, and social profiles.
- Publish helpful content around those questions. A simple website with FAQ-style pages and use-case guides gives AI tools more context about your product.
- Get mentioned in public places. Share your build journey in relevant communities, as this founder did on Reddit. Genuine discussions create more references to your app.
- Charge from day one. Even a small price tells you whether the product is worth building further.
- Ask new users how they found you. Add a one-question onboarding survey with an “AI assistant (ChatGPT, etc.)” option so you can measure this channel instead of guessing.
- Post short-form video consistently. Pick YouTube Shorts, TikTok, or Instagram Reels and commit to a regular schedule you can sustain.
- Run a real creator outreach sprint. Build a list of niche micro-creators, send personalized messages with a clear offer, and track replies.
- Review weekly and double down. Whatever channel actually drives paying users, put more time there and cut what isn’t working.
Want to Get Your App Recommended by AI Assistants?
Start today by asking two or three AI chatbots the exact question your ideal user would ask, like "best app for [your niche]." Write down which products they recommend and how they describe them, then rewrite your own product description to answer that question just as clearly.
Source: week two of my soccer app!
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