A conversation with Ted Staeb on recruiting beyond keywords, where AI can improve the candidate experience, and why human curiosity and judgment still matter more than a match score.
Host: Adam Kleckner (Head of Strategy at LinkTech), Devon Walker (Head of Recruiting at LinkTech)
Summary:
Ted Staeb loves recruiting because the puzzle never stays solved. The roles change, the people change, and the real work is figuring out what you’re missing. In this conversation, he explains why listing a tool isn’t the same as demonstrating a skill, how asking candidates to teach you something creates better conversations, and where he sees genuine potential in AI screening. He also shares a telling example: two recent hires scored below 40% in a matching model—and were exactly what the team needed.
Main Topics:
Why curiosity separates keyword matching from understanding people
Compensation as the starting point—not the whole motivation
Connecting a role to the business problem it exists to solve
AI-generated resumes: useful structure, unreliable claims
The “grandma question” for explaining complex technical work
AI screening vs. interviewing—and why the distinction matters
Human review of candidate rankings and transferable skills
Hiring friction, candidate communication, and accountability for ghosting
Intriguing Quotes:
“Money is what opens and closes every single door for a candidate. It’s not why you walk through the door, but it’s why you get to it.”
“Every single role at a company doesn’t exist in a vacuum.”
“Two of the hires I’ve made in the last month scored under 40% on the model, but they were exactly what we were looking for.”
“I love problems. Like that is arguably why I got into recruiting.”
Key Moments:
[05:27] Questions and context: understanding what a role contributes to the business gives recruiters a better way to discuss the opportunity—and candidates a clearer picture of success.
[06:39] An unexpected upside of AI resumes: Ted welcomes more consistent formatting, while Devon challenges the exaggerated or inaccurate claims that can come with it.
[10:57] The elevator example: two electricians list the same skill, but only one explains the scale and complexity of what they’ve delivered. Context changes the evaluation.
[12:50] The “grandma question”: asking someone to explain their work simply helps the recruiter learn and reveals how the candidate communicates technical ideas.
[19:19] Ted’s case for AI screening: replacing binary qualification questions with space to explain “no, but…”—not replacing a meaningful human interview.
[26:55] Human in the loop: AI can point a recruiter toward relevant information, but Ted’s two hires with low match scores show why the model shouldn’t make the decision.
[30:35] Where hiring gets stuck: the hiring manager, job description, recruiter, and candidate can all be working from different expectations. Ted argues for transparency and empathy across the process.
[38:53] Lightning round: Ted would retire reference checks, cautions against treating referrals as a guarantee, and says recruiters need to own their ghosting.
[44:36] What never appears on Ted’s resume: the persistent urge to find a problem and figure out how to fix it.
Concepts: Human-in-the-loop AI; recommendation engines; contextual skills assessment; candidate motivations; technical storytelling; hiring-process alignment
Community mentioned: Unstuck job-search workshop
Connect with Ted Staeb:
LinkedIn: https://www.linkedin.com/in/tedstaeb/
Connect with The Human Advantage Podcast:
Website: https://www.thelinktech.com/podcast