CoderPad

Based on the CoderPad webinar featuring Bayo Ojuri (CoderPad), Bonnie Dilber (Zapier), and Melany Austin (GoDaddy). “AI fluency” has quietly become a line item in nearly every technical job description. The problem: most teams added the requirement long before they had a reliable way to assess it. You can ask a candidate whether they use AI. What you cannot easily see is whether they use it well.

Our CEO, Amanda Richardson, and our Head of Product, Frank Hauben, can’t agree on anything else. We put Amanda and Frank on camera, handed them five spicy hot takes about AI in hiring, and told them not to sit on the fence. They sat on the fence anyway. Here’s what they actually fought about. Amanda hires for the next ten years. Frank hires for next Tuesday. Both are right, which is exactly why this is hard. Here are the five fights, and the one thing they finally agreed on.

We put a room full of senior engineering leaders together, took away the slides, added beer, and asked them to say the thing they’d never put in a LinkedIn post. No PR-approved talking points, no vendor gloss, just people who ship for a living telling the truth about what AI is doing inside their orgs right now. It got spicy. Here’s what actually came out of the room. The loudest claim in the market got some pretty quick pushback.

A YC-backed founder called me last week with some good questions. His 20-person startup hadn’t hired an engineer in over a year. Now they’re opening reqs again, and instead of just dusting off the old loop, he wanted to understand how interviews had changed and if candidates were ready for AI interviews. His process — one coding challenge, one system design, one product conversation — is this still the right way to find great engineers?

Every hiring team eventually hits the same wall: TA own raising the bar, don’t have the technical knowledge required to write questions that test for it. So teams are improvising, a shared question bank, a busy engineer’s spare 20 minutes, and last year’s assessment recycled one more time. It feels fine butt isn’t. Recycled and leaked questions are easy for candidates to prepare for or just solve with AI. Static question banks can’t keep pace with how fast job requirements change.

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.