Let’s talk about AI pair programming - not the hypothetical future version, but what we have right now in 2024. I’ve spent the last year working extensively with AI coding assistants, and I have some thoughts to share.
Don’t Knock It Until You Actually Try It
The most vocal critics of AI pair programming often share one common trait: they haven’t seriously tried it. I’m not talking about asking it to write a fibonacci sequence and laughing when it makes a mistake. I mean actually incorporating it into your daily workflow for a week or two.
It’s like that person who insists they don’t need a debugger because “print statements work just fine.” Sure, they do work. But once you actually learn to use a proper debugger, you realize what you’ve been missing.
Trust, but Verify
Of course, you shouldn’t blindly trust your AI partner. Just like you wouldn’t blindly trust code from Stack Overflow or accept a human colleague’s PR without review. The AI will make mistakes, sometimes spectacularly obvious ones, and other times subtly wrong ones.
But here’s the thing - this isn’t a weakness, it’s a strength. Verification is part of our job. We verify everything. The fact that you need to verify AI-generated code isn’t a strike against it; it’s just applying the same professional standards we always should.
Test-Driven AI Development
Want to really level up your AI pair programming? Write your tests first. Not only does this follow good TDD practices, but it gives you an immediate verification system for the AI’s output. The tests become your specification, and the AI becomes your implementation partner.
When the AI gets it wrong (and it will), you have immediate feedback. When it gets it right - you already know it’s right, because your tests pass.
“But the Code Looks Different!”
Here’s a controversial take: for a significant portion of our code, if it passes the tests and maintains readable structure, does it matter exactly how it’s written?
Sure, for your core business logic, your critical algorithms, your performance-sensitive paths - be as opinionated as you want. But for the thousands of lines of CRUD operations, data transformations, and utility functions? If it works, is maintainable, and passes review - who cares if an AI wrote it?
The Economics Are Undeniable
Even if AI pair programming only makes you 50% faster (and in many cases, it’s more), that’s an enormous win. Companies will always optimize for developer throughput - it’s simple economics. If you can ship features faster while maintaining quality, that’s a competitive advantage no business can ignore.
The question isn’t whether to adopt AI pair programming - it’s how quickly you can effectively integrate it into your workflow.
Today’s Models Are the Worst You’ll Ever Use
This is perhaps the most exciting part: the AI models you’re using today are the worst ones you’ll ever use. They’re the Model T of AI programming assistants. And yet, they’re already incredibly useful.
Every iteration brings improvements. The hallucinations decrease, the understanding of context improves, the ability to work with larger codebases gets better. If you’re waiting for them to be “good enough,” you’re missing the point - they’re already good enough to provide value, and they’re only getting better.
Remember the Container Skeptics?
“Containers are just lightweight VMs. They’ll never work in production.” “Docker is just a toy. Real enterprises won’t use it.” “Kubernetes is too complex. It’ll never catch on.”
Sound familiar? We’ve heard similar arguments about every transformative technology in our industry. The same people who scoffed at containers, cloud computing, and version control are now saying AI coding assistants will never work.
History has a way of humbling technology skeptics. The question isn’t whether AI programming assistants will become a standard part of development workflows - it’s how long will you wait before embracing the inevitable?
The Bottom Line
AI coding assistants aren’t perfect. They’re not going to replace developers. But they are an incredibly powerful tool that can make you more productive right now. Not in some hypothetical future - today.
The developers who learn to effectively work with AI tools will have a significant advantage over those who don’t. Just like those who embraced git, containers, and cloud computing early had an advantage.
Don’t be the last person on your team still insisting that “vim and grep” are all you need. The future is here - it’s just not evenly distributed yet.