How I Built a $10K-a-Month Software Business With AI

A few years ago, I had no practical way to build the products I imagined.

I understood products. I had spent years thinking about users, markets, features, and growth. As a product manager at Alibaba, I worked on a consumer app with more than 100 million monthly active users. At that scale, even a small product decision can affect millions of people.

The job taught me how to study user behavior, recognize unmet demand, and understand why people adopt one product while ignoring another.

But there was one thing I could not do.

I could not build the product myself.

I could write a product requirements document, map the user journey, define the features, and explain exactly how the product should work. But turning that plan into functioning software still required engineers, designers, money, and coordination.

For a long time, most of my ideas remained documents.

AI changed that.

Today, I have built eight iOS apps and two SaaS products. Together, they generate around $10,000 a month in product-based income.

That did not happen because I typed one prompt and AI created a company for me. I did not become an expert engineer overnight, either.

What changed was more practical and, in some ways, more important: AI shortened the distance between product thinking and execution.

It gave me a way to take an idea, turn it into software, put that software in front of users, and continue improving it without waiting for a full technical team.

That shift has changed how I think about entrepreneurship. The opportunity is not simply that AI can write code faster. The larger opportunity is that more people can now build businesses around their own ideas.

Start With Friction, Not Technology

A common question in the current AI boom is: “What can I build with AI?”

I think that is usually the wrong place to begin.

When technology comes first, it is easy to create something that looks impressive but solves no meaningful problem. The demo works. The landing page looks polished. People may even share it. But no one has a strong reason to return or pay.

I prefer to start with friction.

What takes too long?

What feels unnecessarily complicated?

What are people repeatedly complaining about?

What requires five different tools when one focused product could do the job?

Some of my product ideas came from problems in my own life. Others came from customer emails, online communities, user reviews, or weaknesses I noticed in existing software.

Negative reviews are especially useful. People are often very clear when a product disappoints them.

They say things like:

“This app is too complicated.”

“I only need one feature.”

“I don’t want to create an account.”

“I wish this worked on my phone.”

“I would pay for this if it saved me more time.”

These are not just complaints. They are clues.

A good product idea does not need to be completely original. It needs to solve a specific problem for a specific group of people.

At Alibaba, I learned that users rarely care how sophisticated the system is behind the screen. They care whether the product helps them achieve what they came to do.

That is still the first question I ask:

Who is this product for, and what result are they trying to achieve?

Build the Smallest Product That Delivers a Result

Once I find a problem, I resist the temptation to build a large platform around it.

Instead, I ask a narrower question:

What is the smallest version of this idea that can create a useful result for one person?

This matters even more now because AI makes overbuilding dangerously easy.

You can keep adding pages, dashboards, settings, integrations, and models. Each new feature feels like progress. In reality, it may be making the product harder to understand.

More software does not always mean more value.

I try to begin with one user, one problem, and one main action.

Then I use AI to help me turn the idea into a product plan. I define the target user, the main problem, the shortest path to value, the essential features, and everything I can remove from the first version.

From there, I create a simple product requirements document. I map the screens, the user flow, the data structure, and the minimum version I want to launch.

This planning stage matters because AI performs much better when the problem is clearly defined.

“Build me a complete SaaS platform” is not a useful instruction.

It is too broad. The result is often inconsistent, unnecessarily complex, and difficult to maintain.

A better approach is to give AI context, assign one task at a time, and define what success looks like before any code is written.

AI is powerful, but clarity is still a human responsibility.

Use AI as a Collaborator, Not an Autopilot

The term “vibe coding” has become popular because it captures something real: people can now describe what they want in natural language and use AI to produce functioning software.

But the term can also create the wrong impression.

Vibe coding does not mean accepting every piece of generated code without understanding or testing it.

AI can write code, explain unfamiliar systems, fix bugs, design databases, connect APIs, create landing pages, and help prepare App Store releases.

It can also make serious mistakes.

It may change code that was already working. It may solve one problem and create another. It may introduce security risks, duplicate logic, or make the architecture more complicated than necessary.

For that reason, I treat AI as a collaborator rather than a magic button.

I usually ask it to study the project before making changes. I give it one task at a time. I ask it to explain what it intends to do. I test important changes, and I use version control so I can return to a stable version when something breaks.

I did not begin this journey with a technical background. But building real products gradually taught me how software systems work.

I learned how to ask better questions. I learned to recognize when an answer did not make sense. I learned how to isolate a problem, test a hypothesis, and move forward in smaller steps.

That is one of the most valuable things AI can offer non-technical builders.

It does not remove the need to learn. It allows you to learn while creating something real.

You do not need to understand everything before you start. You do need to develop judgment as you go.

Launch Before You Feel Ready

Many new builders spend too much time preparing for users they do not yet have.

They keep improving the design. They add more features. They rewrite the onboarding. They tell themselves the product is almost ready.

But until a real person uses it, most of their assumptions remain untested.

I try to release a small version early.

The first users may come from people I know, online communities, Reddit, LinkedIn, Facebook Groups, Discord, or direct outreach.

For an iOS app, the App Store itself can become a discovery channel. For a SaaS product, search traffic, content, partnerships, or free tools may matter more.

There is no universal acquisition channel. It depends on the product and the user.

What tends to remain consistent is that early user acquisition is manual.

I ask people to try the product. I watch where they become confused. I read their messages. I look at what they use, what they ignore, and where they leave.

I want to know whether they understood the product, reached the main value quickly, returned later, and felt enough value to pay.

The first ten users are not simply an early audience.

They are part of the product research.

Their behavior often reveals more than weeks of planning.

Marketing Is Not Something That Happens After Launch

One of the most expensive misconceptions in software is that a useful product will naturally find users.

Usually, it will not.

Developers often spend most of their energy on building and very little on distribution. But if no one knows the product exists, the quality of the code is irrelevant.

I now think about marketing before the product is finished.

Where does the target user already spend time?

What would they search for?

What problem would make them click on a tutorial?

What free resource could bring them into the product?

What frustration can I explain more clearly than my competitors?

AI helps me with this work. I use it to research keywords, organize customer feedback, develop content ideas, draft marketing materials, and adapt one idea for different platforms.

But AI does not decide what the market cares about.

That still requires observation, judgment, and contact with real users.

Marketing is not separate from the product. It is part of how the product is understood.

A product that cannot explain its value will struggle even if the underlying technology is excellent.

The Real Advantage Comes From Repetition

I did not build multiple products by starting from zero each time.

Every project created something reusable.

A technical structure.

A launch checklist.

A design component.

A marketing workflow.

A pricing lesson.

A better understanding of App Store optimization, SEO, onboarding, email, and user retention.

The first product is usually the hardest because everything is unfamiliar.

The second becomes slightly easier.

After several products, you are no longer just building software. You are building an operating system for yourself.

That operating system is where much of the leverage comes from.

Not every product succeeds.

Some products attract users but produce little revenue. Some need to change direction. Some require more maintenance than expected. Some fail completely.

But together, the products form a portfolio.

Some generate income. Some bring traffic. Some build an audience. Some teach me how to enter a new market. Some create technology or knowledge I can reuse elsewhere.

Today, that portfolio generates around $10,000 a month.

I describe it as product-based income rather than fully passive income.

Software still needs maintenance. Users still need support. Platforms change. Marketing continues. Products break. Reviews can turn negative. An update can create a problem you did not anticipate.

The benefit is not that the business requires no work.

The benefit is leverage.

A software product can serve many people without requiring me to deliver the service individually to each customer. The same work can continue producing value after the initial development is finished.

Over time, that creates more freedom, more career options, and less dependence on a single source of income.

A Second Income Is Also a Form of Security

AI is arriving at a moment when many people feel uncertain about work.

Layoffs have become common. Career paths feel less predictable. People are increasingly unsure whether their current skills will remain valuable over the next several years.

I do not think everyone should quit their job.

I do not think everyone needs to become a full-time founder.

But I do believe more people can build something of their own.

It could begin with a small app, a useful website, a digital product, or one additional source of income.

A second income stream is not only about earning more money.

It can create choices.

It can reduce the fear that your future depends entirely on one company, one manager, or one position.

It can also change how you see yourself. You are no longer only someone who performs a role inside another organization. You become someone who can identify a problem, create a solution, and bring it into the market.

That confidence has value even before the income becomes significant.

Belief Is Useful Only When It Produces Action

I believe in manifestation, although perhaps not in the way the word is often used.

For me, manifestation is not simply imagining a result and waiting for it to appear.

It is the decision to treat an idea as possible, followed by the repeated actions required to make it real.

AI has not removed uncertainty from entrepreneurship. It has not made every idea valuable or every product successful.

What it has done is make experimentation cheaper, faster, and more accessible.

That matters.

A person with product knowledge, industry experience, creativity, or insight into a particular customer problem can now do more without waiting for permission, funding, or a full team.

The challenge is no longer only whether you can build.

It is whether you can choose the right problem, create something useful, reach the right people, and continue improving when the first version does not work.

Those are the questions I plan to explore in this series.

I will write about finding product ideas, validating demand, building with AI, launching, marketing, pricing, and growing revenue.

I will also write about the less polished parts: failed experiments, bugs, poor reviews, incorrect assumptions, and products that gained users but made no money.

The real process is more useful than another perfect success story.

You do not need to start with a team.

You do not need a large budget.

You do not need to understand everything before you begin.

You can start with one problem, one user, and one small product.

That is how a one-person AI business begins.