Monetizing AI Projects: 5 Real Paths That Work
And the Pitfalls to avoid
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This blog!
A vibe coded gacha game
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Faceless YouTube Channel
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The AI gold rush is well underway. Every day, new GPT-powered tools hit the web, promising to automate, accelerate, and monetize just about anything. Stories float around about side projects that make $10,000 a month or AI-powered newsletters growing overnight.
But for every success, there are dozens of forgotten apps, stale prompts, and unpaid Gumroad pages.
Over the last year, I’ve been testing the waters, if you’re a subscriber you’ll be well aware of just how tough some of these hustles can be!
I’ve been building tools, launching experiments, and studying what’s actually working. After digging into projects that succeeded (and several that didn’t), I’ve identified five core monetization paths that seem to consistently deliver results.
Let’s dive into them.
1. 🧰 Productized AI Tools
One of the most popular strategies is building a lightweight tool powered by GPT, Claude, or another model. The idea is to solve a specific, recurring problem, ideally for a niche audience, and charge for access.
For example, Legalese Decoder translates contracts into plain English using GPT, while MagicForm helps teachers turn any text into quizzes and assessments. These tools aren’t overly complex, but they serve a clear purpose and save time.
Pros:
Scalable once the product is live
Great potential for recurring revenue with a SaaS model
Strong fit for niche audiences with specific pain points
Cons:
The market is saturated with GPT wrappers
You'll need more than a clever prompt to stand out
Users can often replicate your tool on their own if the value isn’t obvious
Bottom line: To succeed, your product has to offer more than access to a model. Look for ways to build workflow, structure, or expertise into the experience.
1.5 🤖 Vibe Coding: Letting AI Code What You Can’t
One of the most fascinating developments in AI right now is the rise of "vibe coding", a term describing the practice of building software without deep technical knowledge, by relying on AI models to write and explain code for you.
If you're not a developer (or only lightly technical), this can feel like unlocking a cheat code. You describe what you want, and tools like GPT-4 or Claude write the functions, generate the UI, fix bugs, and even walk you through the logic — all in real time.
Vibe coding is often messy, chaotic, and trial-and-error. You’re working from intuition, vibes, and prompts instead of formal knowledge. But remarkably, it works, at least well enough to build prototypes, apps, websites, and automations.
Why it matters:
Vibe coding lowers the barrier to entry for building tech products. If you can dream it and describe it clearly, you can start building it, even without knowing how to code; or without knowing exactly what you’re doing. Many creators are launching tools and apps this way.
Examples:
A non-coder launching a GPT-powered resume tool with just ChatGPT, Replit, and Gumroad
Creators using GPT to generate full-stack apps via AI engineer agents
Indie founders iterating on entire products with GPT + Vercel + Supabase
Pros:
No need for deep programming expertise
AI can walk you through logic, help debug, and even explain your own code
Ideal for prototyping MVPs, tools, and SaaS experiments
Cons:
You’re still responsible for what the AI builds — and debugging can get tough
Security, scalability, and architecture decisions may be weak or missing
Harder to maintain long-term if you don’t deepen your coding knowledge
Bottom line:
Vibe coding is the gateway drug to indie AI development. You don’t need a CS degree to start building — just a vision, a prompt window, and enough curiosity to ask “what if?” The best way to learn is to do, and vibe coders are already launching real businesses, tools, and side projects at scale.
2. 🧠 Teaching Others AI
With so much hype and confusion around AI, there’s a huge need for clear, approachable education. Whether you’re just getting started or already experimenting with prompts and workflows, there’s likely someone a step behind you who wants to learn.
That’s where teaching comes in. You can create online courses, publish a paid newsletter, run live workshops, or post educational content to platforms like YouTube or TikTok. Even simple prompt libraries or template packs can sell if they’re well organized.
Examples include:
Learn Prompting, a free course used in classrooms
Rob Lennon’s AI-focused courses and paid communities
AI tutorial channels like AI Explained
Pros:
Huge and growing demand for accessible guidance
Builds audience trust and long-term credibility
Educational content continues to generate value after it’s published
Cons:
High upfront effort to create meaningful, polished materials
Content can become outdated quickly as AI evolves
You're building a media presence, which requires consistency and engagement
Bottom line: You don’t need to be a world-class researcher. If you can explain what you’re learning in a way that helps others, you’re already ahead of the curve.
3. ✍️ AI-Powered Content Creation
AI is changing the way content is created and distributed. Tools like GPT, Claude, and even ElevenLabs allow solo creators to write blogs, generate newsletters, script YouTube videos, and more, often in a fraction of the time it used to take.
Some creators are running fully automated content businesses. Others use AI to streamline their process while keeping a human voice in the mix. From faceless YouTube automation channels to affiliate blogs and Twitter threads, the range of possibilities is broad.
Pros:
Producing content becomes faster and less labor-intensive
Opens the door to passive income through ads or affiliate links
A great way to test new ideas or find an audience
Cons:
Generic AI content won’t cut it anymore — quality still matters
SEO is tightening up around AI-generated spam
Monetization takes time and typically requires consistent output
Bottom line: Use AI as a creative partner, not a replacement. The best results still come from blending automation with originality and insight.
4. 👨💻 Freelancing and AI Services
If you’re not ready to build your own product or audience, there’s still a straightforward path to making money: offer your skills directly.
You can help businesses implement GPT-based workflows, automate customer support, generate SEO content, or create internal tools. Prompt engineering, chatbot design, and custom automations are in demand — and companies are willing to pay for them.
This route works well for developers, designers, marketers, and even generalists who are comfortable with tools like Zapier, Make, or Airtable.
Pros:
Fastest way to start earning with AI
No need for a product, website, or following
Great learning opportunity that can lead to other income streams
Cons:
Income is tied to your time and availability
Harder to scale without turning it into an agency or product
Client management can be challenging and time-consuming
Bottom line: Freelancing is often the first step. It helps you learn what people actually want; and gives you real-world experience that can feed into products or courses later.
5. 💸 Selling AI Projects or Digital Assets
Not every project has to scale into a company. If you enjoy building, you can treat your AI experiments as digital assets — then sell them once they gain traction.
Platforms like Flippa, Acquire.com, and even Gumroad let you list everything from small SaaS tools to eBooks, prompt packs, and niche content sites. If your project solves a real problem or has an active user base, it can fetch real money.
I’ve sold a GPT-powered tool for over $100K. Others have flipped AI newsletters, Discord bots, or Midjourney guides for solid four- or five-figure exits.
Pros:
A way to monetize projects that you no longer want to grow
Great for recouping time spent on experiments
Selling gives you the space and resources to start fresh
Cons:
Projects need polish, documentation, and a clean handoff process
Buyer demand can vary depending on your audience size and revenue
You have to be emotionally ready to let go
Bottom line: Think of your side projects as prototypes. If something clicks but you don’t want to scale it, list it. Someone else might want to take it further.
Final Thoughts
The most successful creators and builders aren’t locked into a single monetization path. They combine two or three. For instance, you might build a product, then teach how you made it, and eventually sell the assets. Or you might freelance until one of your internal tools becomes a viable product.
What matters is momentum. Choose the path that matches your energy, skillset, and time — then build from there.
Until the next time, I’d love to hear from you:
Which monetization path are you most curious about?
Have you tried one already? What happened?
Reply to this post or share your story in the comments. Let’s learn by building, together.