"With great power comes great responsibility." — Uncle Ben, the original vibe check.

I’ve been thinking a lot about how AI coding assistants have changed my day-to-day workflow over the past couple of years. I went from Codeium to Copilot to Claude and a handful of others in between, and the one thing I keep coming back to is this: these tools are amplifiers. They’re not replacements, they’re not autopilot, and they’re definitely not magic. They take whatever signal you feed them and make it louder.

I know the "AI is coming for your job" takes are everywhere right now. I don’t buy it. What I’ve seen — in my own work and across teams I’ve worked with — is something more nuanced and, frankly, more interesting. These tools don’t replace the developer. They reveal what kind of developer is holding the controls.

Plug in a skilled guitarist and you fill a stadium. Plug in someone who’s never held a pick and you fill that same stadium — with noise.

What Amplification Actually Looks Like

When I’m in the zone — when I know what I’m building, how I want to get there, and what "done" looks like — these tools let me move at a pace that wasn’t possible even two years ago. The bottleneck used to be how fast I could hit the keys. Now I’m moving almost at the speed of thought. Boilerplate that used to eat an afternoon gets generated in seconds. Test scaffolding materializes on demand. That regex I’d normally spend twenty minutes on Stack Overflow debugging? Done before I finish describing it. It’s kind of like having the most productive pair programming buddy you’ve ever worked with, except this one doesn’t drink your coffee.

And it’s not just the rote stuff. When I’m exploring a new approach or thinking through how to structure a module, I can have a conversation with the assistant about trade-offs, ask it to sketch out alternatives, and iterate on the design before I’ve written a single line of production code. That feedback loop used to require grabbing a colleague, finding a whiteboard, and hoping they had context on the problem. Now it happens in seconds, inline, without breaking flow.

But here’s the thing about amplification: it’s direction-agnostic. It doesn’t have opinions. It doesn’t tap you on the shoulder and say, "Hey, are you sure about this architecture?" It simply takes whatever direction you’re heading and helps you get there faster. Good direction or bad.

The Uncle Ben Part

This is where the responsibility kicks in, and I’ve seen what happens when it doesn’t.

I’ve watched developers propagate anti-patterns across an entire codebase in an afternoon — work that would have taken weeks to do by hand, and honestly would have been caught somewhere along the way precisely because it was slow. I’ve seen teams accumulate tech debt at unprecedented speed because the AI-generated code "worked" and nobody paused to ask whether it was the right approach. I’ve reviewed pull requests where the developer clearly accepted every suggestion wholesale. The code compiled. It was also architecturally incoherent.

Here’s a concrete example. I watched a developer ask an AI assistant to build out an entire data access layer. The tool happily obliged — generated models, repositories, service classes, the works. Looked great on the surface. But it completely ignored the existing patterns in the codebase, introduced a second ORM alongside the one already in use, and duplicated logic that was already handled by a shared library. The developer shipped it because it "worked in testing." Three sprints later the team was untangling the mess. The AI didn’t make that mistake. The developer did, by not reviewing the output against the system they were actually building in.

Speed without direction isn’t velocity. It’s just chaos with a higher commit frequency.

Three Things That Actually Matter

The developers I see getting the most out of AI coding assistants share three things in common. None of them are about prompt engineering.

First, they know what they’re doing. They have a solid mental model of the system they’re working in. They understand the domain, the constraints, the trade-offs. The AI accelerates their execution, but the thinking was already done — or is being done in parallel, deliberately. You can’t evaluate an answer if you don’t understand the question.

Second, they know how they want to get there. They’re not asking the AI to make architectural decisions for them. They’ve already chosen the pattern, the approach, the boundaries. The AI fills in implementation details within a framework the developer defined. Think of it as delegation, not abdication.

Third, they know what success looks like. They can look at generated code and immediately tell whether it’s right — not just whether it compiles, but whether it belongs. Does it match the conventions of the codebase? Does it handle edge cases? Will the next developer who reads this understand what’s happening? This is the skill that separates developers who use AI effectively from developers who just use AI.

Without those three things, you’re not amplifying skill. You’re amplifying uncertainty. And that’s a problem.

What This Means for Teams

One thing I’ve been paying attention to is how this plays out at the team level, especially with more junior developers. I wrote in a previous post that AI coding assistants could be a boon for junior folks — a pair programming buddy to help them skill up faster. I still believe that. But I’d add a caveat now: it depends entirely on whether the team has a strong review culture. A junior developer using AI without experienced eyes on the output is just the amplification problem at a different scale. The tool doesn’t know the difference between a learning opportunity and a landmine.

The teams that are getting this right tend to treat AI-generated code the same way they’d treat code from any new team member: with trust but verification. Code review isn’t just a gate — it’s where the actual knowledge transfer happens. If anything, AI assistants have made code review more valuable, not less, because there’s simply more code flowing through the pipeline.

The 3 AM Test

Here’s the scenario nobody talks about in the AI hype cycle: it’s 3 AM, your pager goes off, and production is on fire. A P1 is rolling in, customers are affected, and the service your team owns is the one that’s down. You need to troubleshoot — fast. Where do you look? What’s the call chain? What changed recently? Which dependency is the likely culprit?

If you wrote the code — or at minimum, you comprehended the code before you shipped it — you have a mental map. You know the system’s pressure points. You can form a hypothesis in minutes and start narrowing it down. But if you shipped AI-generated code that you never truly understood, you’re reading your own codebase for the first time while the house is burning. That’s not a debugging session. That’s a disaster.

This is the part that doesn’t show up in productivity metrics. Understanding the code you deliver isn’t optional — it’s the insurance policy you’ll need when things go sideways. And things will go sideways. They always do.

Fundamentals Matter More Now, Not Less

There’s an irony in all of this. The rise of AI coding assistants doesn’t make the fundamentals less important — it makes them more important. When you can generate code at scale, the ability to evaluate that code becomes the bottleneck. Code review skills matter more, not less. System design thinking matters more, not less. Understanding the why behind a pattern matters more than ever, because the AI will happily give you the what without it.

The developers who treat AI output as a first draft — something to be read, questioned, and refined — are the ones building better software faster. The ones who treat it as a final answer are building fragile systems at record speed. I can see this category of tooling only getting more capable, and the gap between developers who wield it well and those who don’t is going to widen.

The Signal Is Yours

AI coding assistants aren’t going anywhere. The amplifier is only going to get louder. The question worth asking isn’t whether to use these tools — it’s whether you’re feeding them a signal worth amplifying. Invest in the fundamentals. Know your system. Define what good looks like before you ask the AI to build it.

Uncle Ben was right. The power is real. The responsibility is yours.