THE AI PARADOX: WHY 84% OF DEVELOPERS USE IT, BUT 46% DON'T TRUST IT

So, I got assigned to write this month's Sherpa update, and honestly? I've been wanting to talk about this for a while now.

Let me paint you a picture: It's 9 AM, I'm sipping my coffee, and I'm staring at a blank file. I need to build a new API endpoint for handling Shopify webhooks. Instead of starting from scratch, I pop open Cursor (my AI coding assistant) and ask it to generate the boilerplate. Five minutes later, I have a working endpoint structure. Pretty cool, right?

But then, I spend the next 20 minutes reviewing every single line, testing edge cases, and rewriting half of it because the AI didn't quite understand our specific authentication flow.

Sound familiar?

THE NUMBERS THAT MADE ME THINK

I came across this stat recently that really hit home: 84% of developers are using AI tools now - GitHub Copilot, ChatGPT, Cursor, you name it. We're all doing it. But here's the kicker that made me pause: 46% of us don't fully trust what AI generates.

That's almost half of us. Half of us are using a tool we don't completely trust. That's wild, right?

MY DAILY DANCE WITH AI

Let me be real with you - I use AI tools every single day. They're incredible for:

• Grunt work: Setting up repetitive components, API routes, and configfiles. The stuff that makes you want to pull your hair out.

• Learning curve moments: When I need to quickly understand a new library or framework, AI gives me a solid starting point.

• "What was that syntax again?" moments: Instead of Googling, I just ask and get an instant answer.

But here's where it gets interesting...

THE TRUST GAP IS REAL (AND THAT'S OKAY)

Last week, I asked AI to generate a function for processing payment webhooks. It looked perfect - clean code, good error handling, proper TypeScript types. I almost just copied and pasted and moved on.

But something made me pause. I started tracing through the logic, and boom - there was a subtle security issue. The AI had missed an edge case where a malicious payload could slip through. It wasn't obvious, but it was there.

That's when it hit me: The trust gap isn't a bug, it's a feature. We're not blindly trusting AI, and that's exactly how it should be.

WHAT AI GETS RIGHT (AND WRONG)

THE GOOD STUFF

AI absolutely crushes the routine stuff. Need a standard form component? Done. Need to refactor some messy code? It'll suggest cleaner patterns. Documentation? It'll write comments that actually make sense.

I've saved hours on boilerplate code alone. Hours I can spend on the fun stuff - solving actual problems, building features that matter.

WHERE IT STUMBLES

But here's what I've learned the hard way:

Business logic? AI doesn't understand your specific domain. It can't know that in your e-commerce flow, certain products need special handling, or that your authentication has custom rules.

Security? This is the big one. AI will write code that appears secure, but it may overlook subtle vulnerabilities. It lacks the context of your entire system, your threat model, and your specific requirements.

Performance? AI suggests optimizations, but it doesn't know your traffic patterns, your user behavior, or what actually matters for your specific use case.

Architecture? It'll suggest patterns, but they might not fit your team's style, your existing codebase, or your long-term plans.

THE REAL TALK MOMENT

Here's what I think many people overlook: Using AI doesn't necessarily mean we're saving as much time as we think.

• Sure, AI generates code in seconds. But then we're:

• Reviewing every line carefully

• Testing thoroughly (because we don't trust it)

• Debugging issues that AI didn't catch

• Sometimes rewriting chunks because the approach doesn't fit

But honestly? This is how it should be. We should be reviewing code carefully. We should be testing thoroughly. We should be thinking critically about every line.

AI isn't replacing our judgment - it's amplifying it.

WHAT THIS MEANS FOR YOU (YES, YOU READING THIS)

If you're a developer, here's my take:

Use AI, but use it smart. It's a starting point, not a destination. Always review, always test, always understand what the code does. And know when NOT to use it - critical security features, complex business logic, performance-critical sections.

Develop your AI literacy. Learn to write better prompts. Understand your tool's limitations. Know when AI will help and when it'll just waste your time.

If you're a client or someone working with developers:

AI is making us faster, but it's not making us sloppy. We're still reviewing everything. We're still testing. We're still maintaining quality standards. If anything, we're being MORE careful because we know AI can miss things.

Ask questions. How are we using AI? What safeguards do we have? How do we ensure quality? Transparency matters.

THE BOTTOM LINE

AI in web development is here to stay. It's making us more productive, helping us learn faster, and taking care of the boring stuff so we can focus on what matters.

But the trust paradox? That's not a problem to solve - it's a feature. It means we're being responsible. We're using AI as a tool to enhance our capabilities, not replace our judgment.

We're finding that sweet spot between "AI does everything" and "AI does nothing" - and that's where the magic happens.

WHAT'S YOUR TAKE?

How are you using AI in your workflow? Have you had those "wait, that's not quite right" moments? What have you learned?

Drop me a line, let's chat about it. Because honestly, we're all figuring this out together.

Damian

P.S. - If you're reading this and thinking, "I should try AI tools," do it. But start small. Use it for documentation, learning, and repetitive tasks. Get comfortable with it. Then gradually expand. And always, always review what it gives you.