CoderPad

Artificial intelligence has fundamentally changed software development. Developers are no longer working in isolation, writing every line of code from memory. AI has become part of the engineering workflow, helping developers brainstorm approaches, generate boilerplate code, and debug complex issues. The question isn’t whether engineers are using AI. It’s how effectively they’re using it. Hiring teams now face the same reality.

Back to blog Finding the best technical assessment software for HR teams isn’t about finding the platform with the longest feature list. It’s about finding the platform that helps you identify real skills quickly, fairly, and consistently. The challenge for recruiting and talent acquisition teams is separating signal from noise. A candidate may have an impressive resume or perform well in a traditional interview, but that doesn’t always predict success in the role.

For the last two years, the narrative has been loud, confident, and mostly wrong: AI is coming for entry level engineering jobs. The story goes something like this: If AI can write code, summarize docs, generate tests, and debug basic issues, why would companies still hire junior engineers? It sounds logical. It is also incomplete. Because inside high performing engineering organizations, something very different is happening. The best teams are not walking away from junior talent.

Resumes made sense when applying for a job took real effort. That era is over. AI lets candidates apply to hundreds of roles in the time it once took to write a single cover letter. Employer-side AI systems process the flood. The result is a hiring funnel built almost entirely on artificial signals, where the quality of a candidate’s AI tools matters more than the quality of their work. This is not a future problem. It is happening in your pipeline right now. The signal is gone.

The short answer: Many technical hiring processes are doing the latter. High-friction interview loops, live coding under observation, and AI-restrictive assessments are measuring interview performance, not job performance. The fix isn’t rebuilding from scratch — it’s auditing whether your process measures engineering capability or something else entirely.

Campus recruiting season arrives the same way every year: hundreds of applications, a narrow window, and a process that was never designed for this much volume or this much pressure. Meanwhile, the definition of “technical skill” is actively shifting—AI-polished resumes have made traditional screening nearly meaningless, and the skills that predict junior developer success look different than they did two years ago. Most teams get through it. Fewer teams get it right.

AI has fundamentally changed how engineers work. It’s not a tool candidates might use anymore. It’s embedded in how code gets written, reviewed, and shipped every day. In many teams, the majority of code is already AI-assisted—and growing. Which leads to a clear conclusion for hiring teams: Interviews without AI interviews are becoming obsolete. Trying to remove AI from the interview process doesn’t restore signal—it distorts it.

Not skills. Not judgment. Not potential. It’s screening for the ability to interview well. And those two things have almost nothing to do with each other. We sat down with recruiting experts Cammas Freeman, Phil Strazzulla, and Kaylee Estes to talk through three of the biggest blind spots in technical hiring right now. What they said is worth slowing down for. By the time a candidate walks into your first screening call, they’ve already formed an opinion about your company.

When we opened our latest software engineer role, we had 100 applications within 24 hours. By the end of the first week, without running a single ad, we had 160 candidates in the pipeline. That’s a lot of signal to sort through. And it forced us to be honest about something every engineering leader eventually confronts: a hiring process is only as good as its ability to protect your team’s time while treating candidates fairly. We’re CoderPad. We build technical hiring software.

AI didn’t just change how engineers build software. It changed what it means to hire one. The tools engineers use every day — AI coding assistants, prompt-driven debugging, multi-model workflows — are now fundamental to how modern development teams operate. According to our State of Tech Hiring 2026 Report, 82% of developers find generative AI useful, and 54% would experience a measurable productivity drop if AI tools were removed tomorrow. AI isn’t a productivity booster anymore.