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Arie Abramovici is VP Software at Exodigo.ai.
“Quality is everyone’s responsibility.” (W. Edwards Deming)
The advantages of AI are undeniable, and the technology’s rapid advancement is driving the seismic shift happening in the technical recruitment landscape. As someone who has spent nearly two decades building and leading software teams across military, startup and scale-up environments, I’ve witnessed firsthand how traditional hiring approaches are becoming increasingly outdated. Today’s tech leaders must reconsider their hiring strategies to remain competitive in this new era.
The traditional tech hiring process—scanning resumes, conducting multiple technical interviews and relying heavily on algorithm challenges—was designed for a different era. AI is now transforming every step of this process, from initial candidate sourcing to final selection. More importantly, it’s changing what we should look for in candidates themselves.
Why Traditional Hiring Methods Are Becoming Obsolete
In my experience leading teams at various scales, from a 40-person unit in the IDF’s Intelligence Corps to Waze’s infrastructure team serving hundreds of millions of users, I’ve learned that technical skills alone are no longer sufficient. The emergence of AI coding assistants like GitHub Copilot and powerful language models has democratized many aspects of coding we previously used to evaluate candidates.
When I was building teams at startups, we often focused heavily on testing candidates’ ability to implement specific algorithms or solve complex coding puzzles. While these skills remain valuable, they’re no longer the primary differentiator they once were. AI tools can now generate efficient code, suggest optimizations and even help debug complex problems—tasks that were once the exclusive domain of experienced developers.
The New Essential Skills
Today’s tech hiring should focus on capabilities that AI cannot easily replicate.
AI Literacy And Tool Integration
Engineers need to understand how to effectively collaborate with AI tools, knowing their strengths and limitations. While scaling technical teams, I’ve observed that the most effective engineers are those who can seamlessly integrate AI tools into their workflow while maintaining critical judgment about the output.
System Design And Architecture
While AI can help with implementation details, the ability to design robust, scalable systems remains a uniquely human skill. At Waze, architectural decisions were crucial for supporting rapid user growth, and they required a deep understanding of business context and trade-offs.
Problem Definition And Requirements Analysis
AI excels at solving well-defined problems, but humans are still unmatched at identifying and defining the right problems to solve. The most valuable team members are often those who can bridge the gap between business needs and technical solutions.
Implementing A Modern Hiring Strategy
There’s no denying that the tech world is changing, so tech leaders should adapt their hiring processes accordingly.
Beyond Copy-Paste: Developers Need More Than Just Coding Chops
First, you should redefine the skill a software engineer needs to succeed in this day and age. AI will generate code similar to the code of existing systems. When you’re building “another one of those,” it’s great. However, to be able to do something that’s never been done, solve difficult problems or choose a solution that best fits your company’s situation. your engineers need the ability to learn, understand the business needs, ask questions and apply critical thinking.
Goodbye, Bubble Sort; Hello, AI Whisperer
Second, redesign technical assessments to evaluate candidates’ ability to leverage AI tools effectively. Instead of testing whether someone can implement a sorting algorithm from scratch, assess how they approach problem-solving with AI assistance.
Architecture Over Algorithms: Building Teams That Last
Third, focus interviews on system design, architecture decisions and real-world problem-solving scenarios. During my time leading teams, I found that candidates who excel in these areas often become the most valuable team members, regardless of their coding speed.
The Never-Ending Game Of Assessment Cat-And-Mouse
Finally, continuously reassess the efficacy of your hiring process. For example, Google used brain-teaser interviews until its data analysis showed it’s a bad predictor of first-year performance.
Tools (including AI tools) to assess the value of CVs will have reduced accuracy when facing CVs that AI has rewritten. You might want to fine-tune them to look for things AI cannot “prettify.” AI can solve handed-in home assignments (and you should encourage the candidate to use it), so you might want to change the assignment to one that doesn’t have a textbook “best” solution.
AI tools can also quickly and easily solve many existing interview questions, so you should constantly reassess how good your interview questions are to sort the right candidates.
Looking Ahead
As we navigate this transformation, it’s crucial to remember that AI is not replacing human engineers—it’s augmenting their capabilities. The most successful organizations will be those that can identify and nurture talent that complements AI’s capabilities rather than compete with them.
The future of tech hiring isn’t about finding developers who can outcode AI. Rather, it’s about finding those who can work alongside it most effectively—bringing human creativity, judgment and strategic thinking to the table. As leaders in the tech industry, we must adapt our hiring practices to reflect this new reality.
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