Something’s been bugging me about how new devs and I need to talk about it.

We’re at this weird inflection point in software development. Every junior dev I talk to has Copilot or Claude or GPT running 24/7. They’re shipping code faster than ever. But when I dig deeper into their understanding of what they’re shipping? That’s where things get concerning.

Sure, the code works, but ask why it works that way instead of another way? Crickets. Ask about edge cases? Blank stares.

The foundational knowledge that used to come from struggling through problems is just… missing.

We’re trading deep understanding for quick fixes, and while it feels great in the moment, we’re going to pay for this later.

A couple of days ago, Cursor went down during the ChatGPT outage.

I stared at my terminal facing those red error messages that I hate to see. An AWS error glared back at me. I didn’t want to figure it out without AI’s help.

After 12 years of coding, I’d somehow become worse at my own craft. And this isn’t hyperbole—this is the new reality for software developers.

As a content creator in the tech space, I found myself caught in an all-too-familiar trap: endless hours of doomscrolling through social media and news aggregators, trying to stay on top of the latest trends.

The signal-to-noise ratio was abysmal—for every meaningful tech development, I had to wade through countless memes, heated arguments, and clickbait. I knew there had to be a better way.

Modern software development is complex. Our projects have hundreds of files, intricate dependencies, and carefully thought-out architectural decisions.

Mark Zuckerberg recently claimed AI will replace mid-level engineers by 2025.

As someone building AI developer tools and studying their real-world implementation, I believe this fundamentally misunderstands both the current state of AI and the role of mid-level engineers.

Here’s what Meta is missing.

Learning why model redundancy > optimization

It started with a frustrating Thursday afternoon. Our code analysis service was hitting rate limits constantly, and I was doing what any reasonable engineer would do: optimizing our token usage, implementing better queuing, and trying to squeeze maximum performance from our chosen model.

Nothing worked. Or rather, everything worked a little bit, but not enough.

Last week, my AI coding assistant generated a perfectly-structured code review suggestion.

The format was immaculate - every field properly typed, every attribute carefully specified, the suggestion clear and actionable.

There was just one problem: it fundamentally misunderstood how our authentication system worked.