The Soul-Crushing Reality of Modern Recruitment (And Why We Need to Fix It)

I’ve spent over two decades in the trenches of SaaS product development, watching my creations touch millions of lives across 40 countries. Through years of consulting with companies struggling to modernize, I’ve developed a particular obsession with one area that seems frozen in technological amber: talent acquisition. Let me tell you a story that perfectly captures the absurdity of our current recruitment landscape.

At an HR conference, I cornered a senior sourcing head with what seemed like a simple question: “How long should a resume be?” Her response was textbook: “Ideally 1 page for newcomers, 2 pages for experienced folks, max 3 for veterans.” When I asked why, her answer was painfully honest: “Who has time to read long resumes? We get seconds per candidate with mountains to scan.” That’s when I pushed harder: “So candidates have to guess what to include because you don’t have time?” She bristled: “Well, of course! They need the job, so they have to match my expectations.” (The mask slipped momentarily as she realized what she’d admitted) “And you don’t need them?” I countered. Back pedalling: “We need them too, but we can’t read everything they write.” My final question hit a nerve: “So what are the chances you’re rejecting perfect candidates simply because they couldn’t cram their value into your arbitrary page limit?” Her dismissive response before walking away? “That’s just part of the trade.” This isn’t just her problem. It’s the fundamental brokenness of our entire recruitment ecosystem.

The Tragedy of Digital Photocopying

When HR software emerged, instead of re-imagining processes for the digital age, we essentially photocopied existing human-driven workflows into code. The result? Digital systems that perpetuate the same inefficiencies they were supposed to solve. This explains why organizations over-hire in good times and mass-fire when the economy dips. A manager doesn’t know how to complete a task, so they hire someone who does. That person hires another to handle uncomfortable aspects, and suddenly you’ve got a bloated team with overlapping responsibilities.

And it goes beyond resume scanning. Why do we still conduct performance reviews just once or twice yearly? Why not bite-sized monthly check-ins? Why are employee satisfaction surveys annual events instead of random, situational pulse checks? These questions haunt me.

The Interview Circus

If you think resume screening is bad, let’s talk about the interview farce. Over 80% of non-HR employees conducting interviews have zero training. They’re guided by outdated dogma and the arrogant belief that “only the candidate needs a job.”

This explains why the world’s most common interview opener remains the mind-numbing “Tell me something about yourself.” As the candidate recites their rehearsed life story, the interviewer is frantically thinking, “What the hell do I ask next?” The HR solution? “Let’s train people to interview better!” Noble, but is that really the best answer in today’s AI-driven world?

The Hard Truth About Talent Tech

My obsession with fixing this broken system led me to test 381 different Talent Intelligence platforms (I would’ve tested more, but the rest wouldn’t give me access). Between product trials and patent research, I discovered two uncomfortable truths:

1. The talent tech gap is structural: HR professionals rarely have deep technical expertise, while tech experts seldom understand H.R’s nuanced challenges. These domains remain frustratingly disconnected.

2. Current approaches are fundamentally flawed: Data scientists rely on basic keyword and semantic matching with a sprinkle of machine learning fairy dust. Meanwhile, self-proclaimed “Gen AI experts” simply feed resumes and job descriptions into large language models, ignoring that these systems hallucinate and require sophisticated guardrails to produce reliable results.

Today’s “techpreneurs” are more interested in quick valuations and acquisitions than creating products that could transform how we nurture human potential. It’s a depressing reality.

Garbage In, Garbage Out

The problem with today’s recruitment technology mirrors the limitations of AI itself. AI is essentially a context engine for datasets — but the quality of outputs depends entirely on inputs. If you lock Einstein in a room with no access to information or time to think, even his brilliant mind would produce nothing valuable. Similarly, if you overload someone with random datasets without processing time, you get generic responses. That’s exactly what happens with most LLMs — feed them garbage, they return garbage. The typical two-page resume contains nothing but claimed data — the equivalent of that locked room with minimal information. How can we expect meaningful assessment from such limited input?

At Talent Pulse 360, we’re training our models to read between the lines and, where information is lacking, create relevant connections through controlled research. We’re building a knowledge bank of observed and deterministic data signals that help assess candidates, not just resumes. Because fundamentally, a person is infinitely more complex than a two-page document.

The AI Interview Illusion

I’ve tested numerous platforms offering AI avatars that conduct screening interviews with “top 10 questions” for each role. But here’s what baffles me: who screens these results when AI can’t make binary decisions?

These systems are programmed to remain neutral and inoffensive, generating mountains of interview data that gets passed to the same human recruiters who “didn’t have time” to read three-page resumes! We’ve merely shifted the bottleneck. Most AI interview platforms use general-purpose LLMs trained on public internet data. While impressive in breadth, these models have the focus of a hyperactive toddler in a candy store — they know a little about everything but struggle to deliver precise, relevant insights for specific contexts. Plus, password-protected datasets (where the most valuable information lives) remain inaccessible, and hallucinations remain a persistent problem.

The True Art of Interviewing

Interviewing isn’t just asking questions. As the word suggests, “inter-view” is a two-way exchange — 30 minutes of information-dense interaction that can transform a team’s trajectory. The worst sin is approaching this crucial ritual unprepared, defaulting to “Tell me about yourself.”

Real interviewing is a complex dance: hearing, listening, comprehending, empathizing, analyzing, and deriving insights to determine fit.  It means breaking the ice, creating comfort, asking relevant questions that show you value the candidate’s time. It involves reading tonal shifts, facial expressions, body language, and verbal contradictions — then probing deeper to reveal what’s hidden. Is this humanly possible to do consistently? Probably not. But a well-designed intelligence engine could achieve it. Just as we use calculators for speed, confidence, and accuracy rather than mental math, an intelligent system could handle these complexities while humans focus on what truly matters. The key is avoiding “AI interview” platforms that are merely fancy wrappers around general-purpose language models.

A Better Way Forward

In these chaotic times, we need to be a guiding light for job seekers navigating an increasingly complex landscape. At TalentPulse360, we’re building a thinking model with empathy, compassion, intelligence, and analytical rigor — one that can augment human capabilities tenfold in selecting the right people for the right roles. We need to acknowledge that our current talent acquisition systems are relics from another era. We have unprecedented technological capabilities that could transform how we identify and nurture human potential — if only we’re brave enough to reimagine the processes themselves rather than digitizing their flaws. The future of recruitment isn’t just about better technology — it’s about fundamentally rethinking what we’re trying to accomplish and designing systems that respect both the complexity of human potential and the realities of organizational needs.

The talent is out there. The question is whether we’ll build tools worthy of finding it.