Most leaders want better hires, faster. But as AI reshapes recruiting, the deeper issue is what hiring systems are actually selecting for, and what they quietly reward.

Chris Fanchi speaks with Fletcher Wimbush, CEO of Discovered, about end-to-end recruitment automation, structured talent assessment, and the tradeoffs leaders face as hiring becomes more scalable and less human-driven. They discuss bias, candidate experience, feedback risk, the future of the resume, and a provocative possibility: that removing humans from parts of the process may improve fairness and decision quality in certain roles.

At the center is a durable leadership principle: as skills become easier to simulate, integrity, motivation, and judgment become harder to ignore.

Most leaders want better hires, faster. But as AI reshapes recruiting, the deeper issue is what hiring systems are actually selecting for, and what they quietly reward.

Chris Fanchi speaks with Fletcher Wimbush, CEO of Discovered, about end-to-end recruitment automation, structured talent assessment, and the tradeoffs leaders face as hiring becomes more scalable and less human-driven. They discuss bias, candidate experience, feedback risk, the future of the resume, and a provocative possibility: that removing humans from parts of the process may improve fairness and decision quality in certain roles.

At the center is a durable leadership principle: as skills become easier to simulate, integrity, motivation, and judgment become harder to ignore.

Construction is supposedly "behind" on technology. But talking to people actually implementing AI in that world reveals something most industries haven't admitted yet: the friction isn't about the tools. It's about what happens to expertise pipelines, trust relationships, and human judgment when you automate the work that used to teach people how to think.

Eric Helitzer spent a decade as both a subcontractor and general contractor before building SubBase, a procurement software for trade contractors. What he's learned watching AI hit manual workflows maps directly onto what's happening in white-collar work right now. Companies celebrating revenue growth without headcount growth. Junior roles that just never get filled. Apprenticeship systems quietly hollowing out because AI does the entry-level work now.

We discuss why invisible job displacement happens before layoffs, the collapse of apprenticeship pipelines when junior work gets automated, where AI genuinely improves work versus where it creates new dependencies, and why educational communication matters more than the technology itself. This isn't really about construction; it's about what happens when automation meets work that's always been learned through doing, relationship-based, and trust-heavy.

The pattern is already visible. Most people just aren't naming it yet.