You can build and launch an AI application that makes money. A medical student built a CPR training app earning $85 per month per user. A finance professional in Japan generated $34,000 from AI tools. A marketer created a referral tool that brought in $20,000.
The technical barrier to building AI apps has collapsed. What used to require a team of engineers, months of development, and tens of thousands of dollars now happens in days or weeks using AI-powered tools that turn plain English descriptions into working applications.
But here's what most guides won't tell you: building the app isn't the hard part anymore. The hard part is making decisions that determine whether your app actually launches and makes money—or joins the graveyard of abandoned prototypes that demo beautifully but break under real usage.
This guide covers the complete process from idea to launch, focusing on what actually matters now that AI handles the technical complexity. You'll learn how to validate your idea before building, choose the right development approach, and ship a production-ready app with payments working, authentication functional, and infrastructure that scales.
The new reality of AI app development
Something fundamental changed in how software gets built. AI-powered tools now handle the full stack—frontend, backend, database, authentication, payments, hosting—from plain English descriptions. You describe what you want, and the tool builds it.
The infrastructure that used to require specialized knowledge now comes built-in.
- Authentication systems that took weeks to implement correctly work with a single prompt
- Payment processing that required Stripe integration expertise happens automatically
- Database setup that needed a backend developer configures itself
- App Store submission that demanded certificates, provisioning profiles, and Xcode knowledge happens with one click
The limiting factor shifted from technical ability to clarity—knowing what to build, for whom, and why they'll pay for it. The tools can handle 100,000+ lines of code with automatic refactoring, build mobile apps for iOS and Android alongside web apps from the same project, and integrate AI models like GPT and Claude without requiring API keys or configuration.
But capability isn't the same as success. The difference between apps that make money and apps that get abandoned isn't the tool—it's the decisions made before and during the build.
Before you build: The decisions that determine success
Most guides jump straight to the build process. That's a mistake. The decisions you make before you write your first prompt determine whether your app has a chance at success or is doomed from the start. Three decisions matter most:
- Defining who pays and why
- Choosing your revenue model
- Validating demand before you invest serious time
Define who pays and why
Start with the person, not the product. Who specifically will pay for this app, and what problem does it solve that's painful enough to justify payment?
This sounds obvious, but most failed apps skip this step. They start with a feature idea ("an AI that summarizes articles") rather than a person with a problem ("busy professionals who need to stay informed but don't have time to read everything").
Ask yourself: would someone pay $10 per month for this? Not "would they think it's cool" or "would they use it if it were free." Would they reach for their credit card? If you can't confidently answer yes, you need more clarity before building.
Niche beats broad for first apps. The narrower your focus, the easier it is to understand what your users actually need and to reach them with your message.
Choose your revenue model early
How you'll make money isn't an afterthought—it's a design constraint that shapes every decision you make during the build.
- Subscriptions (users pay monthly or yearly) work best when your app provides ongoing value—productivity tools, content platforms, services that improve over time
- One-time purchases (users pay once) work best for tools that solve a complete problem—utilities, converters, specialized calculators
- Usage-based pricing (users pay per action or credit) works best when value scales with usage—API calls, generations, processing
Your revenue model affects tool selection, feature prioritization, and user experience design. If you're building a subscription app, you need a tool that makes recurring payment integration simple. If you're building a usage-based app, you need to track and display credits. Deciding early prevents retrofitting later.
Validate before building
The fastest path to failure is building something nobody wants. The fastest path to success is confirming demand before you invest serious time.
Talk to five potential users before you build anything. Not friends who'll tell you what you want to hear—actual people in your target audience. Ask about their current solutions, their frustrations, what they've tried, and what they'd pay for something better.
A landing page with an email capture can validate interest overnight. Describe the app you're planning to build, explain who it's for, and invite people to join a waitlist. If nobody signs up, you've learned something valuable without building anything. If fifty people sign up in a week, you have both validation and your first potential customers.
Signs your idea is ready to build:
- You can describe your target user specifically
- You've talked to real people who have the problem
- You have a clear answer to "why would someone pay for this"
Signs your idea needs more refinement:
- You're describing features instead of problems solved
- You haven't talked to anyone who would actually use it
- You can't explain why existing solutions don't work
Choosing your development path
Once you know what you're building and for whom, the next decision is how you'll build it. Three main paths exist, each suited to different situations.
AI-powered app builders
AI-powered app builders turn natural language descriptions into complete applications. You describe what you want; the tool builds the frontend, backend, database, and handles deployment. Examples include Anything, Bolt, Lovable, Replit, and v0.
This path works best if you can't code and want a production-ready app. When evaluating tools, look for built-in infrastructure that handles everything from database setup to App Store submission automatically, production deployment capabilities including real payment processing, mobile and web capability from the same project, and reliability at scale with automatic refactoring for growing codebases.
Avoid tools that require separate setup for databases, auth, or hosting. Be wary of marketing focused on speed-to-demo rather than speed-to-production. If code export is the only path to production, the tool isn't designed to get non-technical builders to launch.
AI builders typically cost $20–$200 per month depending on usage and features. Total cost to launch a production app: under $500 for most projects.
Custom code with AI assistance
Custom code with AI assistance means writing code yourself, with AI tools like Cursor, GitHub Copilot, or Claude helping with generation and debugging. This path gives you maximum control but requires coding knowledge.
The cost is primarily your time, plus modest subscription fees for AI coding tools ($20–$50 per month for most). You'll also need to set up and pay for your own infrastructure, which adds complexity but gives you full control.
Hiring developers or agencies
Hiring developers or agencies means paying someone else to build. This typically costs $40,000 or more for a complete app and takes months.
This path makes sense if you have specific requirements that exceed what AI builders can handle—highly custom algorithms, unusual integrations, strict compliance requirements—or if you have budget but no time and your project is genuinely complex. Verify that your requirements actually exceed AI tools' capabilities before committing.
If you go this route, vet agencies carefully for production delivery. Ask for examples of apps they've shipped to the App Store with working payments, not just design portfolios.
From description to working app
You've validated your idea and chosen your approach. Now comes the actual build process. With AI-powered tools, building works differently than traditional development—it's more like a conversation than a specification exercise.
Starting with a clear description
AI builders work from natural language descriptions. The quality of your output depends heavily on the quality of your input. Describe your app the way you'd explain it to a smart friend who's never seen it. Include what the user is trying to accomplish, the main screens or sections they'll interact with, and what data needs to be stored and retrieved.
A good starting description: "A habit tracking app where users can create daily habits, mark them complete, see their streaks, and get reminders. Users should be able to sign in with Google so their data syncs across devices. There's a home screen showing today's habits, a history view showing past performance, and a settings screen for managing habits and reminders."
Leave out edge cases, advanced features, and perfect design initially. Your first version should be the simplest thing that works.
The iterative build loop
Building with AI tools works best as a conversation, not a specification dump. Describe → Generate → Test → Refine. Start with your core description. Let the tool generate a first version. Try it yourself. Note what works and what doesn't. Describe the changes you need. Repeat.
Small iterations beat big specifications. Instead of describing every feature upfront, start with the core user flow. Get that working. Then add authentication. Get that working. Then add payments. Each iteration is a checkpoint where you can verify the app still functions correctly.
AI agents handle debugging and error correction automatically. When something breaks, describe the problem: "The login button doesn't respond when I tap it" or "Users aren't seeing their saved data after closing the app." The tool will diagnose and fix the issue.
Adding core functionality
Each core feature follows the same pattern: describe what you want in plain English, let the tool build it, then move to the next feature.
- Authentication: "Add Google login so users can sign in and their data syncs across devices." This single prompt typically produces working authentication.
- Data storage: "Users should be able to save their favorite items and see them when they return." This creates the database structure, the save logic, and the retrieval logic automatically.
- AI features: "Add a feature where users can paste text and get an AI-generated summary." The best tools have AI integrations built in, no API keys required.
- Payments: "Add a subscription screen where users can upgrade to Pro for $9.99 per month using Stripe." This creates the payment flow, the subscription logic, and the paywall.
Design iteration
The best AI apps don't look AI-generated. They look like human designers created them.
Default AI designs often fail because they apply generic patterns without considering context. Tools with custom-trained design models produce better output. They understand spacing, typography, color relationships, and visual hierarchy in ways that generic AI doesn't.
Design iteration within AI tools works the same as functional iteration. "Make the buttons larger and more prominent," "Use more white space between sections," "Change the color scheme to feel warmer and more inviting." Describe what you want; let the tool implement it.
When to care about design: after core functionality works. Polishing visual design before confirming that users want what you're building is a common trap.
The production checklist
A working prototype isn't a launched product. The gap between "it works on my screen" and "it works when real customers use it" is where most apps die. You build something impressive, show it to friends, get excited—then discover authentication breaks when users try to reset passwords, payments fail silently, or the app crashes when traffic spikes.
Authentication testing
Authentication is the most common failure point in AI-built apps. It's also the first thing real users encounter.
Test the complete login flow, not just the happy path:
- Sign up with a new account
- Sign out
- Sign back in
- Try signing in with the wrong password (should show helpful error)
- Reset your password
- Sign in with the new password
- Sign in on a different device
- Delete your account
Edge cases matter: what happens when sessions expire? When users switch devices? When they close the app and return hours later? If authentication feels shaky after testing, address it before launch.
Payment verification
Testing payments with test cards is not optional. Stripe provides test card numbers that simulate successful charges, declined cards, and various error conditions.
Verify the complete flow:
- User selects a plan.
- User enters payment information (use Stripe test card 4242 4242 4242 4242).
- User gets charged (or sees appropriate error for declined cards).
- User gains access to paid features immediately.
- User receives confirmation email.
Test what happens when the subscription renews, when the card is declined on renewal, and when the user cancels.
Connect Stripe to your actual bank account before launch. Many builders run successful tests with Stripe's test mode and forget to switch to live mode.
Error handling and reliability
Things will break. The question is whether they break gracefully.
Automatic error recovery is one of the significant advantages of production-ready AI builders. When database queries fail, payment processing times out, or API calls return errors, the app should recover without crashing.
Test on actual devices, not just preview screens. The preview in your builder tool runs under ideal conditions. Real devices have slow networks, interrupted connections, and competing applications.
The 3 a.m. test: will this work when you're asleep and a customer is using it? Production apps need to run unsupervised.
App Store submission
Getting your app into the App Store used to be a significant hurdle. AI builders with cloud-signed submission handle this automatically.
What you need to prepare:
- Screenshots of your app in action (Apple requires specific sizes)
- Compelling description, a privacy policy (required by both Apple and Google)
- An age rating, and app category selection.
Common rejection reasons: crashes or bugs, placeholder content, broken links, privacy policy issues, and incomplete information. Timeline expectations: Apple typically reviews apps within 24–48 hours.
Hosting and legal basics
Custom domains make your app look professional. Most AI builders offer custom domain support on paid plans. Production infrastructure should scale automatically—if your app gets featured somewhere and traffic increases 10x, everything should keep working.
Before launch, ensure you have a privacy policy (required by app stores and most jurisdictions) describing what data you collect, how you use it, and how users can request deletion. Terms of service define the rules for using your app and limit your liability. For most apps, template documents are sufficient for launch.
Launch and first revenue
Your app is production-ready. Now the goal is getting it into users' hands and confirming your pricing works.
Soft launch strategy
Start with a small audience. Your first users are beta testers, even if you don't call them that. They'll find problems you missed, ask for features you didn't consider, and help you understand whether your app actually solves the problem you intended.
Find your first ten users through your existing network, relevant communities, or direct outreach. These should be people in your target audience, not just friends who'll try anything you make.
Testing your price
Start with a price. Any price. Free apps don't validate willingness to pay—they validate willingness to try. A user who pays you $10 has told you something much more valuable than a user who downloads for free.
If early users convert at a reasonable rate (5–10% of signups for most apps), your price is in the right range. If nobody converts, either your price is too high, your value proposition isn't clear, or you're reaching the wrong audience.
Raising prices is easier than most builders think. If your early users love your app and you're at $10/month, try $15 or $20 for new users. Grandfather existing users at their original price while testing higher prices with new signups.
Measuring what matters
Focus on three metrics initially.
- Activation rate: What percentage of signups complete the core action that delivers value? If users sign up but never create their first habit or complete their first session, you have an onboarding problem.
- Conversion rate: What percentage of users who experience value go on to pay? This tells you whether your pricing and paywall placement work.
- Retention: Do users come back? A productivity app that users open once and abandon isn't solving a real problem.
Gathering actionable feedback
Ask what users do, not just what they think. "Do you like it?" invites polite affirmation. "How often do you use it?" reveals actual behavior.
Watch where users get stuck. If you can observe a user's first session, you'll learn more than from any survey. The fastest path to product improvement is eliminating friction.
Iteration after launch
Add features based on actual usage, not assumptions.
- Expand (new features, new user types) when your core users are satisfied and usage is stable.
- Deepen (improve existing features, reduce friction) when users are engaged but encountering obstacles.
The compound effect of weekly improvements: an app that gets 10% better every week is dramatically better after three months.
Recognizing when to pivot
Not every app succeeds. Signs that your current approach isn't working:
- Users sign up but don't activate (onboarding or value proposition problem)
- Users activate but don't convert (pricing or paywall problem)
- Users pay but churn quickly (product doesn't deliver ongoing value)
- You can't find users despite targeted outreach (market or positioning problem)
If you've tested multiple fixes for the same problem without improvement, consider whether your core assumption needs to change. A pivot isn't failure; it's incorporating what you've learned.
Real builder examples
The process described above isn't theoretical. These stories share a common pattern: clarity about the problem, willingness to ship imperfect first versions, and iteration based on real user feedback.
- Dirk Minnebo — Founders Table: Ten years in go-to-market consulting, non-technical background. Built Founderstable.club—a platform that matches founders for dinners based on a custom algorithm. Also built an encrypted chat app and "The Dinner Game" (100+ conversation starter questions). Four apps in one month, first version live within days. First dinner: 100% of attendees wanted to come back. Waitlist hit 50+ people.
- William Sayer — TakeawaysApp: Professional mountaineer—climbed Everest, Manaslu, Ama Dablam. No development experience. Built TakeawaysApp.io—an app to capture key insights from conversations, tag people, and set reminders. Went from idea to App Store in two months. "It's so empowering now that creativity is the limiting factor, rather than tech knowledge."
- Bailey — Aura Vista: Data and operations background at Daimler, Nike, and Joyly. Built Aura Vista—a full toolkit platform for spiritual professionals (tarot readers, energy workers, modern mystics). Features include AI-powered daily tarot card pulls, custom tarot spreads, journals, intentions and energy tracking. The problem solved: spiritual healers were stuck using outdated pay-by-the-minute phone platforms from the 1980s with no real place to run a business or build community.
Your next step
Describe your app idea in one paragraph as if explaining it to a friend. Include who it's for, what problem it solves, and why they'd pay for it. If you can write that paragraph clearly, you're ready to validate. If you struggle, you need more conversations with potential users.
Your first app won't be your best app—but it might be the one that proves you can build something that works and makes money. That proof changes everything. Get started with Anything.
