AI RISK ANALYSIS

> TRANSMISSION START:

> There's a growing trend of people using AI to build entire business applications without any coding experience. It's tempting—you describe what you want, the AI generates code, and suddenly you have a working product. But this approach comes with serious risks that aren't always obvious until something goes wrong.

> Let's talk honestly about AI in software development—what it's great at, where it falls short, and how to use it effectively.

The Reality of AI-Assisted Development

> Here's the thing: AI coding tools are incredibly powerful. They can write functions, generate boilerplate code, suggest solutions to problems, and speed up development significantly. For experienced developers, they're game-changers. But there's a crucial difference between using AI as a tool and relying on it to build your entire project.

Where AI Excels

> AI models are fantastic at:

> - Writing repetitive code quickly

> - Suggesting solutions when you're stuck

> - Explaining unfamiliar concepts

> - Generating initial drafts of components

> - Catching simple syntax errors

> - Speeding up development for people who already know what they're doing

> When an experienced developer uses AI, they can move faster while maintaining quality because they understand what the AI is doing and can spot when it makes mistakes.

Where AI Falls Short

> The problem is that AI doesn't truly understand what it's building. It predicts what code should come next based on patterns, but it doesn't grasp the broader implications or security requirements.

> Here are common issues that appear in AI-generated applications:

> Security is often superficial.

> Admin pages might have no actual authentication—anyone with the URL could access sensitive data. AI might create a login page but fail to secure anything behind it. Credentials sometimes end up in client-side code where anyone can see them.

> Critical validation gets skipped.

> AI might calculate prices or process payments on the user's browser, which means anyone could manipulate what they're paying. It fulfills the requirement of "show the user the price" but doesn't understand that you can't trust the client side for financial operations.

> Good architecture gets ignored.

> Sometimes the database is set up properly with security measures and server-side validation, but the AI-generated frontend code just doesn't use any of it. It takes shortcuts that bypass robust infrastructure, creating vulnerabilities.

The Knowledge Gap Problem

> This is where things get tricky. If you don't have development experience yourself, you won't know what questions to ask or what to look for. You might see a working application and assume everything is fine. Meanwhile, there could be fundamental security flaws or architectural problems that will cost you down the line.

> Someone without coding experience can't effectively review AI-generated code because they don't know what good code looks like, how security should work, or what constitutes best practices. And honestly, that's completely understandable—that's why software engineering is a specialized skill.

A Better Approach

> We're not saying don't use AI. We use it ourselves all the time. But here's what works:

> Let experienced people lead.

> If you're building something for your business, work with someone who understands development. They can use AI to work faster, but their expertise ensures the foundation is solid.

> Understand what you're building. Even if you're not writing the code yourself, you should understand the architecture and key decisions. A good developer will explain these things to you in plain language.

> Review and question everything.

> Before any code goes live, it should be reviewed by someone who knows what they're looking for. This applies whether it was written by AI or by a human.

> Use AI as an assistant, not an architect.

> AI is brilliant at handling specific tasks within a larger project. It's not great at making high-level architectural decisions or understanding complex business requirements.

The Bottom Line

> Building software is about more than making something that looks like it works. It's about creating systems that are secure, maintainable, and reliable. AI can help you get there faster, but it can't replace the understanding and experience that comes from actually knowing how to build software.

> If you're considering using AI to build a business application, ask yourself: if something goes wrong, will I know how to fix it? Can I explain how the security works? Do I understand the data flow?

> If the answer is no, you might want to bring in someone who does. It'll save you headaches (and potentially money) down the road.

> The good news? AI tools are making it easier than ever for experienced developers to build great software quickly. You just need to make sure there's actual expertise guiding the process, not just someone passing your requirements to ChatGPT and hoping for the best.

> ---

> A note on transparency:

> I used an AI model to help write this article. I had the story and the insights from my experience, but my thoughts were messy and hard to organize. The AI helped me structure everything clearly and express what I was trying to say. That's exactly the point—AI is an incredible tool when you have the expertise and understanding to guide it properly.

> If you've had something built quickly and cheaply and you're not quite sure if it's solid, it's worth getting it reviewed by an experienced developer. Catching problems early is always better than dealing with them after they've affected your customers or your bottom line.