I've been using AI coding tools since early 2023 when I was introduced to ChatGPT. It's been an interesting journey. Especially in 2025, when major corporations started laying off programmers in the name of the AI revolution. Some of that was likely correction from overhiring during and after COVID, but I'll leave that discussion for another time.

What I can't ignore is that AI has impacted people's lives, and for many programmers, that impact has been negative. New graduates entering the field and experienced developers alike have had their careers upended. I'm not here to have a philosophical conversation about corporate responsibility. I'm here to talk about how we're not utilizing AI for what it was intended to be, and how it could be beneficial for everyone: programmers, clients, corporations, and society.

The promise of AI development is compelling: faster delivery, lower costs, code that practically writes itself. And for certain tasks, that promise is real. But there's a gap between the marketing and the reality that most vendors won't talk about.

What the Data Actually Shows

I'm not here to bash AI. I use it every day. But we need to be honest about what we're seeing in production environments.

Code Rabbit's research found AI-generated pull requests average 10.8 detected issues versus 6.4 for human-written code. That's 1.7x more errors requiring review and correction. Other studies show 20-45% more security vulnerabilities in AI-generated code.

Here's the part that surprised me: junior developers see real productivity gains with AI, around 30-35% faster on basic tasks. But senior engineers using tools like Cursor were actually 19% slower. Why? Because experienced engineers spend their time reviewing, correcting, and rewriting AI suggestions that looked helpful but missed critical context.

In complex systems, that review burden adds up. Senior engineers report 8 to 11 additional hours per week dealing with AI-generated code that doesn't account for their specific architecture.

The pattern is clear: AI accelerates simple, well-defined tasks. It struggles with complexity. And complexity is where most enterprise development actually happens.

The Real Issue: Context

AI isn't stupid. It's actually remarkably capable. The problem is that without detailed guidance, AI defaults to generalizations. It pattern-matches against common solutions it has seen. Those generalizations work fine for tutorials and greenfield projects. They fall apart in production systems with years of accumulated business logic.

Here's an example from my own work. I was building an application and later realized I needed multiple user roles: Admin, Developer, Manager. Since I hadn't specified roles upfront, AI created separate tables for admin users and regular users. It worked. But would it scale? Would it be maintainable when we added new roles? Probably not.

When I provided the context I wanted, a normalized model with a roles lookup table, AI implemented it correctly. The issue wasn't AI's capability. It was my failure to provide sufficient context upfront.

This scales in both directions. In complex enterprise systems, one contextual error can cascade through the entire architecture. But when you do provide comprehensive context, AI can analyze and implement faster than any human team.

Security and Edge Cases: Same Story

Security follows the same pattern. AI will generate code that's secure against common attack vectors. But it won't anticipate how your specific application will be targeted unless you tell it. The flip side: provide your threat model and security requirements, and AI will identify vulnerabilities you hadn't considered. It goes deeper than most manual reviews.

Edge cases are similar. AI generates the happy path. It doesn't know about the weird data that shows up at 2 AM, the race conditions under load, the third-party API that randomly changes behavior. But define those boundaries explicitly, and AI will enforce them consistently.

The theme is always the same: AI generalizes. Humans provide context. Together, they produce better results than either alone.

The Real Opportunity

I'm not arguing against AI. I'm arguing for using it correctly.

The goal isn't replacing humans. It's making developers at every level more effective. Junior developers learn faster by seeing AI's suggestions and understanding why they work or don't. Senior architects validate their thinking against AI's comprehensive analysis. The collaboration makes everyone better.

Will we get to full automation eventually? I think so. But today, AI thinks in generalizations. It needs human guidance to move from generic patterns to solutions that fit your specific business.

What Actually Works

The organizations seeing real gains from AI aren't using it to replace developers. They're pairing AI speed with human judgment. Context and guidance from humans. Execution and analysis from AI.

This is the model we built at WAM DevTech. AI handles the coding, analysis, and refinement. Senior architects provide business context, security requirements, and architectural oversight. Neither could achieve alone what the collaboration produces.

Speed without the risk. That's the goal.

The Bigger Picture

Here's something worth considering: every time you correct AI and explain why, you're not just fixing your code. You're participating in a feedback loop.

Within a conversation, AI learns in real-time. Each correction and context you provide makes the next output better. That's why collaboration works. But it goes further than that. When millions of developers provide corrections and context, that feedback eventually makes its way back into training data. The models released six months from now will be better because of the guidance being provided today.

To truly have AI that helps society through revolutionary innovation, we still need humans to guide it to that next level. The collaboration isn't temporary. It's how AI gets better.

Major corporations like Google, Meta, and AWS jumped the gun, pushing AI coding tools as replacements before the technology was ready for that role. We see it differently. This is an opportunity to serve clients better by combining what AI does well today with the human judgment it still needs.

The Bottom Line

AI is a powerful tool. But a tool is only as good as the hands that wield it.

The companies that win won't be the ones that blindly trust AI output. They'll be the ones that pair AI speed with human wisdom. That's not a limitation of AI. It's just where we are today.