How AI Is Changing the Way We Build Software

AI isn’t just a buzzword anymore. It’s actively reshaping how developers build software. From tools like GitHub Copilot to AI agents like Devin, the development process is changing fast. But despite all the excitement, there’s a lot of nuance in how (and when) these tools actually help.

In this post, we’ll look at how AI is influencing software development today, what works, what to watch out for, and how you can start using it the right way.

The AI Shift in Software Development

A Quick Recap

AI tools started going mainstream with the launch of ChatGPT in late 2022. Since then, millions of developers have started using tools like:

• GitHub Copilot for autocomplete and boilerplate code
• ChatGPT for explaining code or brainstorming solutions
• Cursor, Cline, and WindSurf for context-aware coding support
• Vercel v0 and Bolt for turning UI designs into quick prototypes

We use many of these tools ourselves across our dev team. Everyone uses Copilot and ChatGPT daily, but we’ve learned they’re only helpful if you already know what you’re doing.

Where AI Really Helps

• Writing repetitive code
• Generating boilerplate
• Refactoring simple functions
• Creating test cases or documentation

AI helps cut development time significantly when used on tasks that are well-defined and don’t require deep architectural decisions.

Where AI Falls Short

• Writing scalable, maintainable code
• Making architectural decisions
• Understanding long-term system design
• Navigating edge cases and dependencies

If you’re not careful, AI-generated code can look fine on the surface, but break down as soon as it’s pushed to production. We always tell our team: treat AI like a junior dev who works fast, but needs review and guidance.

AI for Juniors vs. Seniors

How Seniors Use AI

Senior developers treat AI as an assistant, not a replacement. They use it to:

• Speed up tasks they already understand
• Get a second opinion on implementation
• Automate repetitive tasks
• Draft quick code ideas for review

They know when to ignore bad suggestions and how to shape prompts for better results.

That’s why when hiring developers, we look for people who’ve built strong fundamentals first, and can later use AI as a productivity boost, not a crutch.

Why We Restrict AI for Juniors

Juniors often try to let AI do the whole task, which leads to:

• Poor code structure
• Lack of understanding
• Difficulty debugging or maintaining code

That’s why we ask them to first build foundational skills using real-world problem-solving and Stack Overflow.

Once they’ve developed strong habits, we slowly introduce AI tools to help them speed up after they understand what’s going on.

Prototyping vs. Production

Using AI for Early-Stage Work

We use AI mostly for building quick demos or prototypes. For example, if a client wants to see how their app might look or function, AI helps us create that in days, not weeks.

But for anything beyond a basic MVP, we don’t rely on AI alone. When scalability and quality matter, experienced developers must lead the project.

Real-World Example

A client of ours, Borna, asked his team to build a project-tracking tool entirely with AI. Two months later, the tool kind of worked, but was nearly impossible to maintain. They restarted from scratch with real developers guiding the architecture, using AI only for small tasks. This hybrid approach delivered a better product in half the time.

Practical Tips for Using AI Wisely

1. Use AI for First Drafts

AI is excellent at giving you a solid starting point, especially for repetitive or boilerplate tasks. But the output it gives is rarely production-ready. Instead of copying and pasting directly into your codebase, treat the result as a rough sketch. Review it carefully, break it down into smaller, maintainable modules, and ensure the logic actually aligns with your architecture. This saves time without compromising quality.

Let AI write your first draft, then:

• Break it into smaller modules
• Add error handling
• Strengthen type definitions
• Write your own tests

2. Keep the Conversation Focused

When working with AI tools, especially chat-based ones like ChatGPT or Cursor, start a new conversation for each distinct task. This ensures the AI isn't confused by irrelevant context. Give it a clear goal, mention the tech stack or file type, and limit each session to a single objective. Think of it as pair programming with someone who gets confused easily, it works best when you’re specific and intentional.

3. Trust But Verify

Never assume the AI knows best. It can generate code that looks clean and professional but breaks in real scenarios. Always run thorough tests, review logic paths, and consider edge cases. This is especially true for things like error handling, async behavior, or third-party integrations. Make AI suggestions go through the same quality assurance processes you would with human-written code.

To highlight just how confidently wrong AI can be: a friend of mine was writing his master's thesis and asked AI to help him reference a book. The AI confidently gave him the book title and the author's name, which sounded totally legit. He then spent over an hour scouring the internet trying to find the book and author, only to discover that the AI had completely made them up. The book didn't exist. Neither did the author. It was all fiction. So, yeah, trust but verify, always.

4. Stay Modular

AI can easily output huge chunks of code. But large, monolithic blocks are harder to test and maintain. Break AI-generated code into modular components with clear responsibilities. Add interfaces, use dependency injection where appropriate, and separate concerns. This gives you the flexibility to reuse or replace parts later, and helps when troubleshooting or scaling.

5. Use AI to Learn, Not Just Build

AI can be an amazing teacher, but only if you ask the right questions. Ask it to explain what a function does. Ask it to walk through a logic flow. Use it to clarify syntax in a new language or spot why a test is failing. For juniors especially, the goal shouldn’t just be faster code, but deeper understanding. Over time, this builds stronger problem-solving instincts and more independence.

What’s Coming Next: Agentic AI

AI tools are getting smarter. We’re now testing agentic tools like Devin or GitHub Copilot Workspace.

These don’t just respond to prompts, they:

• Launch test suites
• Run apps in the browser
• Debug automatically
• Suggest fixes

This shift means we’re moving from prompt-based coding to task-based collaboration, where AI takes initiative. It’s still early, and these tools aren’t reliable enough yet, but the direction is exciting.

Multimodal AI

The next generation of tools will:

• Understand screenshots, designs, diagrams
• Talk via natural language
• Interact with terminals, browsers, and APIs

We’re excited, but cautiously so. Today, these tools are experimental. In a few years? They might be essential.

The Return of Software as Craft

AI helps us build fast. But speed isn’t everything.

Fast demos impress, but real software requires:

• Clean UX and error messages
• Performance on all devices
• Accessibility
• Edge case handling

These details don’t come from AI. They come from developers who care about polish.

AI helps by clearing the boring stuff off your plate. But the best software still comes from humans who pay attention to the small stuff.

FAQs About AI and Software Development

Is AI going to replace developers?

No. AI is a powerful assistant, not a replacement. It helps experienced developers move faster, but it still needs human oversight.

Can juniors use AI effectively?

Not at first. Juniors should focus on fundamentals before using AI, or they risk developing bad habits and weak skills.

What’s the best use of AI in development?

AI works best for:

• Boilerplate code
• Tests and documentation
• Refactoring small pieces
• Prototyping simple features

Are AI agents like Devin useful yet?

They show promise, but they’re still in early stages. For now, they’re more experimental than production-ready.

Will AI lower the cost of building software?

Yes, and it already has in many cases. When used correctly, AI can speed up development by handling repetitive tasks and boilerplate code, which reduces overall project time and costs. But there’s a catch: it still needs to be guided by experienced professionals. Without proper oversight, AI-generated code can introduce hidden issues that end up costing more in the long run.

Final Thoughts

AI is here to stay. Used well, it helps developers work faster, learn more, and focus on what matters: building great software. But it’s not magic. It’s a tool, and like any tool, it depends on how you use it.

Treat it as your assistant, not your replacement. And never stop building your own skills, because that’s what separates the pros from the rest.