Can't Code But Want to Start a Business? The Greatest Opportunity for Ordinary People in the AI Era Has Arrived

Yesterday I was scrolling through social media and saw a mom friend post an income screenshot—she made over $2,800 last month with an AI-powered resume optimization tool. I was shocked. This person who could barely use Excel properly suddenly became an "AI entrepreneur"?

After chatting with her, I discovered she doesn't know how to write code at all—everything was pieced together using existing AI tools.

This made me realize an important trend: In 2025, not knowing how to code is no longer a barrier to entrepreneurship.

The popularization of AI is redefining "tech entrepreneurship." Things that used to require a technical team can now be handled by an ordinary person with a few AI tools. This might be the biggest entrepreneurship opportunity ordinary people will encounter in their lifetime.

Today, let's talk about what entrepreneurship opportunities exist for ordinary people in the AI era?

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Why is this the opportunity for ordinary people?

Technical barriers have really been broken down

To be honest, when I first started learning programming, just setting up the development environment could take me a whole day. To make a simple website, you had to learn HTML, CSS, JavaScript, plus backend, databases, server deployment... each one was a pit.

I remember in 2018 when I wanted to make a small tool for a friend, it took 3 months from learning to going live, and it was still half-finished.

But now it's different. My mom friend, who didn't even know what code was before, did it like this:

Day 1: Chatted with ChatGPT for 2 hours, organized her idea into product requirements Day 2: Used Claude to generate frontend code—didn't understand it, but it worked Day 3: Followed tutorials to deploy on Vercel, then started promoting on social media

Just like that, 3 days, less than $15 spent (mainly domain and API costs), and a working product was online.

The tools are really mature now

Current AI tools are genuinely mature. The ones I commonly use are just these few:

Writing code: GitHub Copilot helps me write repetitive code, Cursor generates entire pages Creating content: ChatGPT writes copy, Claude helps me optimize logic Design work: Midjourney creates images, Canva adjusts details Other stuff: Voice synthesis, video editing—basically every need has a corresponding AI tool

The key is, when you combine these tools, one person can really do the work of what used to require a small team.

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Several directions suitable for ordinary people

1. Content tools: Easiest to get started

I've seen the most projects in this category because the barrier is really low.

Things like social media caption generators, resume optimization assistants, automatic PPT generation—essentially, you take user requirements, throw them to AI, then package the results and return them to users.

I know a college student who made a "paper paraphrasing assistant" last year. Basically just throwing user papers to AI for rewriting, then charging $0.70 per use. Sounds simple, right? But he made over $1,400 in a month.

Why are these projects suitable for ordinary people?

• Low technical requirements, mainly calling AI APIs
• User needs are clear and easy to validate
• Can quickly test and adjust

The key is finding a specific pain point—don't try to make universal tools.

2. Local services: Opportunities with offline businesses

I'm particularly optimistic about this direction because many offline businesses don't understand AI yet, but they definitely have needs.

I have a friend doing exactly this. He made AI ordering assistants for several restaurants that can understand dialects and recommend dishes based on customer preferences. He charges $430 per restaurant and has already signed over 20.

Why does this direction have opportunities?

First, local businesses have insufficient AI awareness—if you package it a bit, you look very professional. Second, competition is relatively low, not like making apps where it's cutthroat. Most importantly, you can charge one-time development fees plus monthly maintenance fees.

Similar opportunities include smart customer service bots, AI fitness coaches, housekeeping booking systems—all packaging AI capabilities into specific services.

3. Education and training: Never lacking demand

Education is definitely a good direction—users have strong willingness to pay, and you can make it a subscription model.

Things like AI English speaking practice, programming learning assistants, exam prep with smart analysis—essentially using AI for personalized teaching.

I've seen someone doing graduate exam prep who just throws past exam questions to AI for analysis, then generates personalized practice questions and explanations. The technology isn't complex, but it really solves students' pain points.

4. E-commerce tools: B2B clients pay well

E-commerce merchants have really strong demand for efficiency tools, and B2B clients pay much better than consumers.

Automatic product description generation, customer service chatbots, review analysis—these are all very practical tools. The key is you can charge based on results, like "help you improve 10% conversion rate, we take a cut" models.

5. Entertainment and social: Young people will pay

This direction is quite interesting—high user stickiness and strong viral potential.

AI avatar generation, virtual chatting, fortune telling, personalized emoji creation—these might not seem serious, but people really pay for them. Especially young people have high acceptance of novel things.

Can be made into paid membership models or pay-per-use.

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How to actually get started?

Step 1: Don't think big, start with small needs

Many people want to make universal AI assistants right off the bat—that's basically suicide.

I suggest starting with a very specific small need, like a tool specifically for writing social media captions, or an assistant specifically for optimizing resumes. Small and beautiful, easy to validate, easy to build.

Step 2: Validate needs before building

Never work behind closed doors. I've seen too many people spend months building something only to find no one wants it.

Simplest validation methods:

1. Post related content on social media, see the response
2. Create a chat group, manually provide services to test the waters
3. Directly ask friends around you if they would use it

Step 3: Quickly build a working version

For tech stack, I recommend this combination:

• Frontend: Generate with Cursor, React or Vue both work
• Backend: Use Supabase, database and APIs included
• Deployment: Use Vercel, one-click deployment
• AI capabilities: Call OpenAI or Claude APIs
• Payments: Integrate WeChat Pay or PayPal

The advantage of this combination is that even if you don't understand technology well, you can follow tutorials and make it work.

Step 4: Find your first batch of users

Channel selection is important:

• Social media platforms are easiest for getting precise traffic, especially for tool-type products
• Video platforms work well for demo-type products with strong visual impact
• Chat groups and social circles rely on word-of-mouth
• Professional forums work well for professional tools with high-quality users

Step 5: Continuously improve based on feedback

Product launch is just the beginning. You need to continuously optimize based on user feedback, then consider adding new features, expanding to new user groups, increasing pricing, or making it a subscription model.

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Common pitfalls

1. Technology worship, ignoring business fundamentals

Many people think using AI guarantees success—this is the biggest misconception.

AI is just a tool; business fundamentals are still about solving problems. You need to think clearly: What problem are you solving? Are users willing to pay for it? Is your solution better than existing ones?

I've seen many people create technically impressive things that no one uses because they don't solve real pain points.

2. Pursuing perfection, never launching

I've had this problem myself. Always wanting to make the product perfect before launching, ending up forever "perfecting."

The right approach is: make a working version first, launch quickly to collect feedback, then iterate based on user needs. Perfection comes from iteration, not from the beginning.

3. Only focusing on product, ignoring operations

Building the product is just the first step. More important is how to acquire users, retain users, and get users to pay.

I suggest spending 50% of time on product and 50% on operations. Many technically-minded people make this mistake, thinking good products will automatically get users, but reality is harsh.

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Some suggestions

1. Start as a side business, don't quit your job

Never go full-time entrepreneurship from the start—the risk is too high.

I suggest using evenings and weekends to build products, validate market demand, and only consider full-time after having stable income. This way, even if you fail, it won't affect your normal life.

2. Learn to use AI tools, but don't completely depend on them

AI tools are indeed powerful, but you need to understand business logic, know how to ask questions, and judge AI output quality.

AI is an assistant, not a replacement. Final decisions and judgments still depend on you.

3. Focus on user value, not showing off technology

Users don't care what advanced technology you used—they only care about: Can it help me solve problems? Can it save me time? Can it help me make money?

Technology is the means; solving problems is the goal.

4. Build personal brand

While building products, also share your entrepreneurship process, output valuable content, and build personal influence.

This way, even if the product fails, your personal brand remains, making future entrepreneurship easier.

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Final thoughts

The AI era has indeed given ordinary people unprecedented entrepreneurship opportunities, but the opportunity window won't stay open forever. As more people enter, competition will become increasingly fierce.

If you have ideas, now is the best time.

Don't wait until you're "ready" to start, because you'll never be completely ready. What's important is taking the first step and learning and growing through practice.

Remember: In the AI era, the biggest risk isn't failure—it's doing nothing at all.

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Do you have any AI entrepreneurship ideas? Or have you encountered any problems in practice? Feel free to share in the comments—I'll pick a few interesting ideas for detailed analysis!