AI Isn’t Replacing Developers—It’s Creating Demand for More
The “AI will replace software engineers” narrative has been beaten to death. Most arguments follow the same thread: if machines can write code, why keep humans in the loop?
But a recent piece by Matt Asay in InfoWorld takes a different angle—and it’s worth your attention. Instead of shrinking engineering teams, Asay suggests AI might grow them, simply because we’ll want to build more software, not less.
And honestly? He might be onto something.
Efficiency Drives Demand, Not Decline
Asay draws on the Jevons Paradox—a principle from economics that says when something becomes more efficient to use, we tend to use more of it. In this case, the “resource” is developer time.
And developer time is getting a serious upgrade:
- 🧠 GitHub’s internal study found Copilot users completed tasks 55% faster with a higher success rate (78% vs. 70%).
- 📈 A study of 1,900+ engineers showed a 13–22% bump in weekly pull requests with Copilot access.
- 🕒 Companies like Microsoft and ZoomInfo report 40–50% time savings on routine dev work.
That’s real acceleration. But instead of replacing developers, these gains are freeing them up to do more—ship more features, tackle new projects, iterate faster.
AI + Outsourced Talent = Exponential Efficiency
What happens when you combine AI-powered workflows with high-quality contract engineering talent for your software development projects?
You get a competitive edge that’s hard to beat:
✅ AI supercharges senior engineers—less time per task, fewer hires per team
✅ Local contract talent enables real-time collaboration—no timezone headaches
✅ Lower cost, higher velocity—scale smarter, not just bigger
The takeaway: AI doesn’t eliminate the need for developers—it amplifies what they can achieve.
The Hidden Complexity of “More”
As Asay points out, we don’t respond to productivity boosts by slowing down. We respond by building more. But “more software” doesn’t just mean more code. It means more complexity—more integration, testing, documentation, security, and maintenance.
All that still needs people—just now, those people are working faster, with broader scope, and AI in the mix.
Gartner forecasts that by 2027, 80% of developers will need basic AI skills. And even today, demand for AI/ML engineers is exploding—up 148% year-over-year, even as generic “developer” job postings have dipped.
This isn’t a contraction. It’s a shift.
Developers Are Becoming Orchestrators
One of the sharpest points Asay makes: developers aren’t just writing code anymore. They’re orchestrating how AI writes code. They’re shaping prompts, debugging AI behavior, deciding what outputs to trust, and building the rails AI runs on.
That’s not replacement—it’s evolution.
So while AI might reduce the effort to write a line of code, it’s also expanding the scope of what’s possible. And that expansion brings new challenges—ones that still demand human judgment, creativity, and collaboration.
What Comes Next?
Sure, in 5–10 years, maybe micro-teams will run full product stacks with AI as their co-pilot. But that’s not today.
Today, we’re building more. We’re building faster. And the teams doing that? They’re growing—not shrinking.
Recommended Reads:
✔️ AI will require more software developers, not fewer – InfoWorld
✔️ Productivity and Competition Effects of AI – AEA
✔️ The rise of the “AI engineer” – Business Insider
✔️ How developers feel about AI tools – Stack Overflow
