Glen Cathey

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Glen graduated with a degree in psychology and decided against graduate school, which left him with a credential and no obvious next step. He worked in a lab for a while. He tried going into business with friends in screen printing. Neither felt like a career, so he moved to Silver Spring, Maryland, and started hunting for work online at the end of 1996, back when doing so was still a novelty.

The Washington Post had recently added an online job section, and Glen was filtering for roles that required no prior experience, since he had none he wanted to build on. Most of what came back was sales, which he did not want. Then he clicked into the site's catch-all 'other' category and found a recruiting job. He had barely heard of the profession. That listing turned into a career in technical recruiting and, eventually, into a body of work on sourcing and talent strategy that the industry now reads and references.

Learning It the Hard Way

Asked about the people who shaped him, Glen offers an answer that is unusually candid for an industry that runs on networking: for roughly the first ten years, there were none. No structured onboarding, no formal training program, no mentor pointing at the next rung. He figured things out alone, testing approaches nobody had shown him, and he credits that necessity for the depth of what he learned. There is no version of his expertise, he believes, that skips the part where he had to work it out from scratch.

He is careful not to romanticize it, and he would not wish the same conditions on anyone joining the field today. But he does draw a useful lesson from it for anyone entering talent acquisition without a sponsor or a playbook: the absence of guidance is not a ceiling. 'You just have to have the goal and the vision and the discipline to pursue it,' he says. It is an argument for self-direction rather than self-reliance for its own sake, and it explains a lot about how he approaches new technology now.

Thinking About AI Since 2010

Glen's first conference presentation ever was at SourceCon in 2010. The session was titled 'Artificial Intelligence Versus Human Cognition,' a title he now describes with affectionate embarrassment, though the framing was ahead of its time. Fifteen years before AI became a standing agenda item in every talent function, he was already asking the only question that matters: what does the machine genuinely bring to the table, and what does it merely imitate?

Large language models, he argues, changed the answer. They have democratized access to enormous amounts of knowledge and reasoning, and they did it on both sides of the hiring market at once. This is not a technology that HR and talent acquisition deploy at candidates. Job seekers have it too, and they are using it to find roles, auto-apply, and tailor resumes at machine speed. That symmetry is what produced the 18,000 applications and the 90 percent that were supposedly flawless. Even well-intentioned, well-understood use of these tools creates a signal-to-noise problem that recruiting has never faced at this scale.

The Case for Candidate-Side AI

For all his caution about volume, Glen lights up on one application in particular. Shortly after ChatGPT arrived, almost four years ago now, what excited him most was role play: the ability for anyone to realistically rehearse for a hiring conversation and get critique and coaching afterward. Before that, practice meant paying a coach or leaning on a well-connected friend or relative. Now it is available to everyone.

The reason he considers this revolutionary is simple. Nobody is a professional at being on the candidate side of the table. It is nerve-wracking, people rarely get enough repetitions to improve, and the gap between how well someone can do a job and how well they can talk about doing it is often enormous. A tool that helps people represent themselves accurately, used properly, closes part of that gap. Glen sees that as one of the clearest positive uses of AI in the entire hiring process.

Advice for 2026: Just Because You Can, Should You?

Glen's guidance for talent leaders heading into 2026 starts from an opportunity and ends in a warning. AI and automation give organizations a genuine chance to reimagine and redesign work, and part of redesigning work can mean eliminating roles. Because people are typically a company's largest aggregate cost, the savings math is the easiest math to do. He has watched several companies do it recently, telling him plainly that they are looking to save $2 to $3 million, and that the savings will come from headcount reduction within talent acquisition specifically.

Saving money is sound business practice. The question Glen wants leaders to sit with is different: just because we can, should we? What else belongs in the equation alongside cost, such as quality, outcomes, and the experience of everyone the function serves? He is not arguing for protecting jobs at the expense of performance. He is arguing that talent acquisition exists to help the business flourish by bringing in the people who serve customers and invent products, and that a purely subtractive strategy tends to forget this.

Which leads to the conviction he saves for last. He wishes the industry would stop accepting the label of cost center. Even when the product a company sells is AI, it still takes people to sell it, improve it, and build the next version that works better. Reframing talent acquisition as a profit center changes the first question a leader asks, from how much can we cut to how much more business can we drive. The second question, he notes, may not involve cutting people at all.