No, Vibe-Coding Apps Won’t Make You $200k/month

Yes, you can replicate any successful app using AI. So, do it and then just stare at it.

Recently, I’ve came across a post on X. The author claimed you can easily start making $200k/mo by simply replicating a successful app, like Reface, using Cursor. He even suggested copying the same strategy Reface used in 2021 to make your clone go viral.

The problem is that it’s 2025. There are hundreds of times more AI videos flooding social media compared to 2021, and back then face-swapping with AI was still novel and exciting.

I keep seeing narratives like this, and honestly, they’re misleading.

Out of curiosity, I tried to find some real examples of successful vibe-coded apps. Several hours in searching Google, Reddit, and even asking GPT-5, Claude, Perplexity, I couldn’t find even 3 solid examples.

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Why we don’t see many successful vibe-coded apps

‘Building’ was never the bottleneck

Finding the right problem to solve, coming up with a relevant solution, acquiring users — those were.

It takes years of eating glass to win in this game.

Even iconic startups struggled for years before they became successful.

Lenny Rachitsky spoke with the founders of Slack, Figma, Airtable, Notion, Canva, Miro and here’s what he found:

• The median time from idea to feeling product–market fit (PMF) was roughly 2 years.
• From a working product to PMF typically took 9–18 months.
• For companies like Figma, Airtable, and Slack, it took 4+ years to find the Product Market Fit.

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And those were the successful startups. Most projects die right after the MVP stage, never reaching a single paying customer.

The idea of simply replicating existing successful apps doesn’t work either. Those companies have MOATs — defensible edges like network effects, talented teams, proprietary data, switching costs, brand trust, etc.

Take Lovable as an example. They famously built their first product in a single weekend. Since then, new clones pop up almost weekly, yet nobody ever hears about them. Meanwhile, Lovable remains the category leader — thanks to network effects.

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Vibe-coding is very early and needs more time

You could argue the movement only appeared about a year ago, once AI models became strong enough to actually write code. And sure, not every successful founder is public about their story. But if vibe-coding successful apps was really a widespread trend, we’d already be seeing more than a handful of wins.

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Competition is harsh

It’s a strange moment: AI has made building software easier than ever, but it’s also made the space brutally competitive — because now anyone can do it.

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Software as we knew it is slowly dying

In my humble view, traditional software as a use-case is slowly dying unless it pivots to agentic use-cases that solve old problems in far more efficient ways.

Saas and marketplace models that still follow the rules of pre-LLM are unlikely to thrive in 2025 and beyond.

That’s why I never understood the excitement of normal people becoming capable of vibe-coding ‘tarpit’ ideas — trying to create a new Facebook or a general CRM in 2025.

I also believe that AI agents are on the horizon, and once they become mainstream, they won’t need to click through interfaces at all.

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Where vibe-coding is actually useful

Validating new product ideas

Vibe-coding is a great way to prototype, build MVPs quickly, and test them.

For example, YC-backed startups like Airweave.ai and Bondapp.io used Lovable to build the first prototypes of their products.

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But YC didn’t accept them because they used Lovable. Those MVPs will probably get thrown out. YC primarily bets on founders. Most teams enter with an MVP that doesn’t have product–market fit — they’re expected to find it during the batch.

So, Lovable is just a tool to iterate faster. Success still comes from talking to users, spotting insights, pivoting, and adjusting strategy.

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Launching new initiatives inside existing companies

Big companies struggle to ship new initiatives quickly. The larger the org, the heavier the bureaucracy, and the slower the teams.

With vibe-coding, a single product manager with an idea can launch it without waiting for engineering resources.

That’s how Qconcursos.com added $3M to their ARR — by shipping an AI-powered tutoring assistant on top of their existing platform.

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Some positivity

Even though I sound skeptical, my goal isn’t to discourage you from building with AI. In fact, I want to highlight two cool examples of vibe-coded startups I came across.

𝗕𝗶𝗼𝗴𝗿𝗮𝗽𝗵𝘆𝘀𝘁𝘂𝗱𝗶𝗼.𝗮𝗶

An AI voice-based autobiography generation tool built with Replit by Brad Lindenberg.

Users share their memories via voice — about a figure, friend, family member, or themselves — and the platform generates their story as a written book, audiobook, or podcast. It dramatically reduces the cost and time of creating a biography, making it accessible to anyone who wants to preserve their life story.

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𝗖𝗼𝗱𝗲𝗴𝘂𝗶𝗱𝗲.𝗱𝗲𝘃

Built with Cursor by CJ Zafir, CodeGuide automates documentation for AI coding projects.

CJ set himself a challenge to launch 12 startups in 12 months and shared the journey publicly on X. The “building in public” approach built him an audience, and when he finally hit the right idea — it stuck.

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I’m excited

Even with the complexity of today’s startup landscape, I’m still genuinely excited about building new products.

Projects like cluely.com, lovable.com, artisan.co, and calai.app prove you can be a nobody today and a tech rockstar tomorrow if you catch the right AI wave.

AI-assisted coding gives you an edge — but only if you stay grounded, focus on real user problems, think about your moat, and build from there.

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Summary

Vibe-coding is exciting, but it doesn’t change the fundamentals of startups. ‘Building’ has never been the bottleneck — the hard part has always been finding problems worth solving, talking to users, and grinding toward product–market fit. Tools like Lovable, Cursor, and Replit make iteration easier, but they don’t create moats or customers.

That’s why the narrative of vibe-coding your way to $200k/month is seductive, but misleading. The real challenge hasn’t changed: discover a painful problem, solve it better than anyone else, and scale.