Let me save you some time: about 70% of what you’re reading about AI-powered development is marketing copy wearing a technical costume.
The remaining 30%? Actually useful. Sometimes genuinely transformative. But good luck figuring out which is which when every tool promises to “10x your productivity” and “revolutionize how you code.”
I’ve spent the last year actually using these tools. Shipping code with them. Breaking things with them. And I have opinions.
The Buzzword Bingo Card Is Full
Let’s start with the terminology that’s infecting every tech blog and LinkedIn post:
“Vibe engineering” — This one’s my favorite. Apparently, we’ve moved past prompt engineering (which was already a stretch as “engineering”) to something even more abstract. The idea is that you set a general direction and let AI figure out the details. In practice, this means “I don’t really know what I’m building, but the AI is outputting something.”
“AI-first development” — Translation: we built the product, then figured out how to cram AI into it. Now we lead with “AI” in every marketing sentence.
“Multi-agent collaboration” — Multiple AI models talking to each other to solve problems. Sounds incredible. In reality, it’s often one model generating code, another model reviewing it, and both confidently agreeing on the same wrong answer.
“Autonomous coding agents” — The promise: AI that can take a ticket from your backlog and ship a feature. The reality: AI that can write a function, maybe a file, but gets confused when it needs to understand how your actual codebase works.
Don’t get me wrong. There’s real capability here. But the gap between the demo and production is the size of the Grand Canyon.
What Actually Works (Right Now)
Let me be fair. After cutting through the noise, here’s what’s genuinely useful:
1. Code Completion That Doesn’t Suck
GitHub Copilot and its competitors have gotten legitimately good at autocomplete on steroids. Not “write my feature for me” good, but “save me 20 keystrokes on this boilerplate” good.
The trick is calibrating your expectations. It’s a fancy autocomplete, not a junior developer. Treat it that way, and you’ll be happy.
Real productivity gain: 10-15% on routine code. Near zero on complex logic. Sometimes negative when you spend more time fixing its suggestions than writing from scratch.
2. Explaining Unfamiliar Code
This is genuinely impressive. Paste in some legacy code you’ve never seen, ask “what does this do,” and get a reasonable explanation. It’s not always right, but it’s a solid starting point.
I’ve used this for:
- Understanding inherited codebases
- Deciphering regex patterns that look like someone sneezed on the keyboard
- Figuring out what that weird bash script from 2014 actually does
Real productivity gain: Significant for onboarding and archaeology. Saves hours of confused staring.
3. Boilerplate and Test Scaffolding
“Write me a Jest test file for this function” usually produces something usable. Not comprehensive tests that catch edge cases—you still need a brain for that—but a decent starting structure.
Same for boilerplate: API route handlers, React component skeletons, database schemas. The stuff that’s mostly pattern-matching anyway.
Real productivity gain: Moderate. You still need to think about what to test. But the typing part is faster.
4. Documentation Nobody Wants to Write
“Document this function” produces surprisingly decent docstrings. “Write a README for this project” gives you a reasonable starting point.
Is it your voice? No. Is it better than the nothing that was there before? Usually.
Real productivity gain: Real, because documentation often doesn’t exist at all. Something > nothing.
What’s Still Broken (Despite the Marketing)
Now let’s talk about the promises that fall apart in production.
The “Senior Engineer Replacement” Fantasy
Some vendors are genuinely claiming that AI can replace senior engineers. This is delusional to the point of being offensive.
Here’s what senior engineers actually do that AI cannot:
- Understand business context and make tradeoffs
- Navigate organizational politics to ship features
- Mentor juniors (who are now competing with AI for the easy tasks that taught them fundamentals)
- Debug production issues at 3am when the symptoms make no sense
- Say “we shouldn’t build this” when the idea is bad
- Design systems that work at scale before they need to
AI can help with pieces of the coding part. It cannot do the job.
The “Context Understanding” Problem
Every AI coding tool struggles with the same fundamental issue: your codebase is weird.
It has conventions that made sense five years ago. It has workarounds for that one database limitation you hit in 2019. It has patterns that exist because that’s how Bob liked to do things before he left.
AI doesn’t understand any of this. It generates code that looks reasonable but violates seventeen unwritten rules. And you don’t know until it breaks something.
The longer your codebase, the worse this gets. That startup demo with a fresh repo? Sure, impressive. Your 500,000-line monolith with a decade of history? Good luck.
The Confidence Problem
AI is confidently wrong with alarming frequency. It doesn’t say “I’m not sure about this.” It presents garbage with the same tone as correct answers.
This is a teaching problem, not a capability problem. Developers (especially juniors) are learning to trust AI output without verification. When the AI is right 80% of the time, that 20% can introduce bugs that take hours to track down.
I’ve seen production incidents caused by AI-suggested code that “looked fine” and passed a quick review. The bug was subtle, the AI was confident, and the reviewer was moving fast.
The “Autonomous Agent” Reality Check
The pitch: “Give the AI a task, let it work autonomously, review the result.”
The reality: The AI goes off on tangents. It makes assumptions that don’t match your codebase. It gets stuck on problems a human would immediately recognize. It “completes” tasks that don’t actually work.
Every “autonomous” coding agent I’ve tried requires constant supervision to produce anything useful. At which point… you’re just using a fancy IDE with more steps.
The Uncomfortable Productivity Question
Let’s address the elephant: does AI actually make developers more productive?
Studies are all over the place. Some show 25-50% improvements. Others show minimal gains. A few show productivity decreases (time spent fixing AI mistakes).
Here’s my take: AI amplifies your existing workflow, for better or worse.
If you have good development practices—clear requirements, modular code, solid testing—AI helps you move faster on the routine stuff.
If you have chaos—unclear requirements, spaghetti code, no tests—AI confidently writes more spaghetti. Now you have chaos at 2x speed.
The productivity gains are real for:
- Experienced developers who know exactly what they want
- Well-structured codebases with clear patterns
- Routine tasks with established solutions
- Greenfield projects with no legacy constraints
The productivity gains are minimal or negative for:
- Developers still learning fundamentals
- Complex codebases with hidden constraints
- Novel problems without clear solutions
- Anything requiring deep understanding of business logic
What Smart Teams Are Actually Doing
The teams getting real value from AI aren’t buying the hype. They’re being strategic:
1. Standardizing on one tool and learning it deeply. Not chasing every new agent or plugin. Picking one (usually Copilot or Cursor) and building expertise.
2. Using AI for the 80% that’s boring, not the 20% that’s hard. Boilerplate: yes. Complex architecture decisions: no.
3. Treating AI output as a first draft, never as final. Everything gets reviewed. Everything gets tested. “The AI wrote it” is not an excuse for bugs.
4. Training juniors on fundamentals first. AI makes junior developers faster at producing code. It doesn’t make them better at understanding what they’re doing. Smart teams are careful about this.
5. Measuring actual productivity, not vibes. Tracking cycle time, bug rates, and deployment frequency. Not just “we feel faster.”
The Bottom Line
AI-assisted development is real and useful. The productivity gains are genuine—for specific tasks, in specific contexts, with specific workflows.
But “vibe engineering” is nonsense. “AI-first development” is marketing. And anyone telling you AI is about to replace senior engineers is either selling something or doesn’t understand what senior engineers do.
Use the tools. They’re good. Just don’t believe the hype.
The developers who’ll thrive are the ones who see AI as a powerful calculator, not a replacement brain. The ones who know when to use it, when to override it, and when to turn it off entirely.
That’s not as sexy as “the future of coding is here.” But it’s the truth.
And in an industry drowning in bullshit, the truth is underrated.
What’s your experience with AI coding tools? Actually useful or mostly hype? I’m genuinely curious—reach out on our contact page or find us on social.
