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AI is reshaping how developers work—and how teams perform
Used effectively, it’s a powerful accelerator. Used carelessly, it introduces risk and confusion.
Junior engineers are increasingly relying on AI tools like ChatGPT to shortcut their work. Many use it without verifying the output, resulting in code that looks functional but lacks depth, structure, or long-term stability.
AI still struggles with complex issues—problems that span multiple files, systems, or design layers. These challenges require human insight: reasoning, context awareness, and architectural thinking.
And this is where strong engineers set themselves apart.
AI doesn’t replace senior developers—it amplifies them.
When a senior engineer understands how to challenge AI’s output, validate solutions, and guide the tool toward better results, their productivity can grow dramatically—2x, even 5x in some cases.
Not all senior engineers operate at this level. But those who do know how to work with AI rather than defer to it. They move fast, write cleaner code, and spot inconsistencies before they hit production.
By contrast, engineers who blindly trust AI create friction. Their output often requires more scrutiny and revision, which places a burden on senior teammates and slows overall velocity.
This makes your hiring decisions even more critical
Technical interviews should reflect the reality of modern software development. Allow candidates to use AI tools during the process—not to make it easier, but to make their approach visible.
The way someone works with AI reveals more than a resume or algorithm question ever could. Strong candidates will craft thoughtful prompts, challenge incorrect outputs, and adjust as needed. Others will default to copying whatever the model suggests without question.
This gap is easy to miss unless you're actively testing for it.
It’s also a good moment to reconsider the kind of problems being used in interviews. Most engineering teams aren’t solving algorithm puzzles all day. They’re delivering real-world software—secure, maintainable, and business-critical.
And in that environment, AI is only valuable when paired with critical thinking.
Hiring practices should reflect that reality.
Look for engineers who question AI’s assumptions, refine its output, and understand when it’s wrong—and why. This is a far more valuable signal than whether someone can reverse a linked list on a whiteboard.
Beyond hiring, it’s just as important to build a culture where junior engineers are encouraged to ask questions early and often. Without that support, they’ll turn to AI to cover uncertainty and may hesitate to ask for help until it’s too late.
Open communication accelerates learning. It also prevents AI from becoming a hidden liability in your codebase.
This is where engineering leadership matters.
AI is already embedded in how we build software. The differentiator now is how well your team understands it, challenges it, and uses it responsibly.
If you are ever looking for a job or expanding your team, connect with Shelly, Yuliia, or Harold on LinkedIn.
Thanks for reading,
CTO Teachings