Hiring Technical Talent in the Age of AI: 6 Insights from Amanda Richardson, CEO of CoderPad

As AI continues to transform software development, it is also forcing organizations to rethink one of their most important business functions: hiring.

Recently, Enrich hosted an executive dinner with Amanda Richardson, CEO of CoderPad in conversation with Tom Griffiths, CEO & Co-founder, Hone, to discuss how AI is changing the way companies evaluate, hire, and manage technical talent.

The conversation covered everything from AI-generated resumes to the future of engineering interviews and the evolving definition of what makes a great engineer.

Here are six insights that stood out.

1. Traditional Engineering Interviews Are Losing Signal

For decades, coding exercises and take-home assignments have been staples of the engineering hiring process. But as AI becomes embedded in developer workflows, those assessments may be measuring something different than intended.

Today, candidates can use AI to generate code, debug solutions, and complete assignments more efficiently than ever before. The challenge for employers is determining whether these exercises still reveal a candidate's underlying problem-solving ability, judgment, and technical depth.

Many leaders at the table agreed that traditional interview formats are becoming less reliable predictors of on-the-job performance.

2. AI Has Increased Noise at the Top of the Funnel

The rise of generative AI has dramatically lowered the effort required to apply for jobs.

Candidates can tailor resumes, draft cover letters, and optimize applications for specific job descriptions in minutes. While this creates more opportunities for job seekers, it also creates challenges for employers.

Several leaders shared that applicant volume has increased substantially, while confidence in the quality of those signals has declined. The result is a larger funnel that often requires more work to identify the strongest candidates.

3. Hiring Has Become an AI-versus-AI System

One of the evening's most discussed topics was the growing reality that AI now exists on both sides of the hiring process.

Candidates are using AI to create applications and prepare for interviews. Recruiters are using AI to source, screen, rank, and prioritize talent. In many cases, AI-generated materials are being reviewed by AI-powered systems before a human becomes involved.

This dynamic is pushing organizations to reconsider where authentic signal can be found and how hiring processes should evolve to preserve it.

4. Engineering Interviews Need to Reflect How Work Actually Happens

Historically, many technical interviews were designed to prevent candidates from using outside resources.

That assumption may no longer make sense.

AI is already part of how engineers build software, research solutions, and solve problems. Rather than banning AI from the interview process, some organizations are beginning to evaluate how effectively candidates use these tools to navigate realistic scenarios.

The future of technical interviewing may look less like a test and more like observing how someone works.

5. Great Managers Will Need New Ways to Measure Performance

AI is changing not only how work gets done, but also how productivity is measured.

Tasks that previously required days can now be completed in hours. As execution becomes faster, traditional indicators such as activity levels, hours worked, or even code output become less meaningful measures of contribution.

Leaders discussed the importance of focusing on outcomes, business impact, ownership, decision quality, and collaboration rather than simply measuring effort.

6. The Definition of a Strong Engineer Is Expanding

Technical skills remain foundational, but they are no longer sufficient on their own.

As AI handles more routine work, organizations are increasingly valuing adaptability, learning velocity, systems thinking, communication, and the ability to effectively leverage AI tools.

The strongest engineers of the future may not be those who can write the most code, but those who can best combine technical expertise, business judgment, and AI-enabled execution.

7. The Need for Strong Coaching and Leadership Remains

AI may increase individual productivity, but doesn't eliminate the need for coaching and people leadership. Engineering managers are still responsible for coaching, career development, performance conversations, and building high-performing teams - activities that require trust, context, and human judgment. As AI takes over more execution and operational tasks, the role of the manager may become more focused on developing people rather than simply overseeing work, making effective coaching an increasingly valuable leadership skill rather than a diminishing one.

More to Explore.

The conversation continued to include topics:

• How organizations can design hiring processes that prioritize authenticity.

• The growing role of live collaboration and simulation-based assessments.

• What AI-native management practices may look like in the future.

• How engineering leaders should think about talent development in an AI-enabled workforce.

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