Hiring Madness - When Hiring Became a Numbers Game

In this episode of The Agile Software Engineering Deep Dive, Alessandro Guida explores how modern hiring has evolved - and why that evolution may not be entirely positive.

What used to be a deliberate process of evaluating a small number of candidates has gradually become a high-volume pipeline, driven by platforms, metrics, and automation. Applicant Tracking Systems and AI tools help manage scale, but they also shape how decisions are made.

This episode examines the consequences of that shift.

Why hiring is increasingly optimized for quantity rather than quality.

Why generic job descriptions attract large volumes of candidates - but not necessarily the right ones.

And why systems designed to filter applications often struggle to recognize real talent.

The conversation also introduces a critical distinction between hiring for specialization and hiring for talent. While specialist roles require precision and clarity, identifying talent requires judgment, curiosity, and direct human interaction.

Beyond processes and tools, this episode reflects on the broader responsibility of engineering leadership. Hiring is not only about filling positions. It is about shaping teams, culture, and long-term capability.

And in the pursuit of efficiency, organizations may be unintentionally damaging their reputation with the very people they hope to attract.

If you are a manager involved in hiring - whether early in your career or with years of experience - this episode offers a perspective on how to approach it more consciously.

Because hiring is not a pipeline to optimize.

It is a judgment to take seriously.

If you enjoy thoughtful discussions about engineering leadership, decision-making, and the evolution of software engineering practices, this conversation is for you.

Please subscribe to this podcast - it’s the best way to support it.

If you are interested in the original article behind this episode, you can also subscribe to the Agile Software Engineering newsletter.

Transcript

SPEAKER_01 0:28

Welcome to the Agile Software Engineering Deep Dive, the podcast where we unpack the ideas shaping modern software engineering. My name is Alessandro Guida. I've spent most of my career building and leading software engineering teams across several industries, and today I want to talk about something that most managers do, but few truly reflect on. Hiring. At first glance, hiring looks like a process. Job descriptions, applications, interviews, decisions, but somewhere along the way something changed. Hiring quietly evolved from a deliberate act of judgment into a pipeline optimized for volume. More applications, faster screening and shorter decisions. And with the rise of platforms like LinkedIn, combined with ATS systems and now AI, the process has become increasingly efficient. But efficiency comes with a cost. Because hiring is not about matching keywords, it is about recognizing people. In this episode, then I want to reflect on how hiring turned into what I would call a numbers game. Why technology, while useful, cannot replace judgment. Why we often confuse specialization with talent? And why many organizations may be missing the very people they are trying to find. This is also a reflection on responsibility, because hiring is not just another process to optimize, it is one of the most important decisions a manager makes. So let's take a step back and look at how we got here and what we might want to change. Let's dive in.

SPEAKER_02 2:16

Oh, we've all been there.

SPEAKER_00 2:17

Right. Just agonizing over it, trying to perfectly match a job description. But what if that is actually the exact thing that keeps you from getting hired?

SPEAKER_02 2:25

It is honestly the defining frustration of the modern professional era. I mean, you're taking your life's work, this carefully crafted representation of your career, and you're just dropping it into a black hole. Exactly. You drop the coin in the digital well, and you don't even hear a splash. You're just sitting there hoping some algorithm decides you are worthy of human eyes.

SPEAKER_00 2:45

Yeah, and we just kind of accept that this is how the game is played now. Like this is just what a playing for a job is. But welcome to today's deep dive because we are going to completely deconstruct this entire system.

SPEAKER_02 2:57

It is so needed right now.

SPEAKER_00 2:58

It really is. Today we are unpacking a really fascinating piece of source material. It's issue number 25 of an agile software engineering newsletter.

SPEAKER_02 3:07

Yeah, it's specifically a chapter from the Handbook of the Young Engineering Manager.

SPEAKER_00 3:12

Right. And it's titled Hiring Madness When Hiring Became a Numbers Game. And I gotta say, this source completely changes how we look at job hunting.

SPEAKER_02 3:20

It's a wake-up call. Truly. It functions as the systemic critique of an industry that has just become completely obsessed with optimization.

SPEAKER_00 3:28

Throughput, efficiency, all that.

SPEAKER_02 3:30

Exactly. And in the process, they've fundamentally forgotten the actual goal of hiring.

SPEAKER_00 3:34

So our mission today is to figure out how we got here. Whether you're a manager who's currently trying to build a resilient team or an applicant who is braving this wildly frustrating job market.

SPEAKER_02 3:46

Or even just someone curious about why corporate processes feel so broken lately.

SPEAKER_00 3:50

Yeah, exactly. We are gonna look at how hiring devolved. We'll explore how it shifted from this careful human judgment call into a mindless, automated pipeline. And, well, more importantly, how we can actually fix it.

SPEAKER_02 4:03

Because to understand the mechanics of what is broken today, we have to establish a baseline, right? We have to look at what hiring used to look like before the digital scale completely took over.

SPEAKER_00 4:12

Oh, the nineties anecdote, yes.

SPEAKER_02 4:14

Right. The author anchors this whole piece with a personal story from the 1990s that honestly feels almost alien by today's standards.

SPEAKER_00 4:21

Aaron Powell The contrast is just staggering. So the author talks about applying to a multinational software company back in the 90s, and they went through five separate interviews. Five. Five. And the critical detail here is that after every single interview, the person they spoke with actually sat down and wrote a detailed, considered report.

SPEAKER_02 4:42

With their impressions, their conclusions, specific thoughts about the candidate's potential. Yeah. I mean, think about the sheer investment of human capital happening behind the scenes in that scenario.

SPEAKER_00 4:52

It's massive.

SPEAKER_02 4:53

It is. And the author points out that as a candidate, your primary emotion in that situation wasn't exhaustion from all the bureaucracy.

SPEAKER_00 5:00

No, it was respect.

SPEAKER_02 5:01

Exactly, respect. The company was signaling through their massive investment of time that bringing a new human being into their organization was a profoundly serious decision.

SPEAKER_00 5:11

Okay, let's unpack this. Because the author then fast forwards a few decades to a recent job application.

SPEAKER_02 5:16

The contrast is harsh.

SPEAKER_00 5:17

It really is. They describe winning what they call the ATS lottery, the applicant tracking system. Right. So their application is ingested by software, it's scored, it gets flagged for a recruiter, and then they are hired after a single interview.

SPEAKER_02 5:32

Aaron Powell Just one.

SPEAKER_00 5:34

Just one. It feels like we moved from this bespoke tailor who, you know, measures you perfectly, notes your posture, asks how you move.

SPEAKER_02 5:43

To a wholesale warehouse.

SPEAKER_00 5:44

Exactly. A warehouse where you're just a barcode on a conveyor belt being scanned by a laser.

SPEAKER_02 5:49

Aaron Powell Well, what's fascinating here is the underlying mechanism driving that shift. It's volume. Platforms like LinkedIn fundamentally alter the physics of the job market.

SPEAKER_00 5:58

They turned it into a supermarket.

SPEAKER_02 6:00

A global supermarket. A recruiter can now broadcast a single role to thousands of potential candidates within hours.

SPEAKER_00 6:06

Aaron Powell, which sounds great on paper.

SPEAKER_02 6:08

Sure, but human beings cannot process thousands of complex professional histories. It's impossible. So to handle the volume, the industry brought in the applicant tracking system.

SPEAKER_00 6:19

Right. The ATS is essentially a semantic filter. It's just parsing your resume, extracting text, and using algorithms to score you against whatever the job description says.

SPEAKER_02 6:35

But notice how that changes the very nature of the decision. When you evaluate five candidates, like in the 90s, you are optimizing your system for judgment.

SPEAKER_00 6:45

You're looking for nuance.

SPEAKER_02 6:47

Nuance potential team fit. But when you evaluate 500 candidates, you can't optimize for judgment anymore. No, you have to optimize for throughput and flow. You build a pipeline. And pipelines are not designed to think critically.

SPEAKER_00 7:01

No, they just move volume.

SPEAKER_02 7:03

Exactly. They filter out anomalies based on rigid, predetermined parameters.

SPEAKER_00 7:08

Which perfectly sets up this terrifying reality we see on professional networks today. I mean, if you spend any time on LinkedIn, you will see recruiters posting what they think is a flex.

SPEAKER_02 7:17

Oh, I know exactly what you're talking about.

SPEAKER_00 7:19

They boast, like, oh, we received hundreds of applications for a single job, and I only have 30 seconds to make a yes or no decision on your resume.

SPEAKER_02 7:26

30 seconds. They present it as a badge of honor.

SPEAKER_00 7:28

Yeah, like it's a sign of how in demand their company is.

SPEAKER_02 7:31

Aaron Powell, When really it's just sloppy, you're accepting wrong decisions just to save time.

SPEAKER_00 7:36

But wait, let me push back on this for a second because you know, from a purely financial perspective, if an ATS filters out 90% of applicants and saves a massive company hundreds of thousands of dollars in recruiter salaries.

SPEAKER_02 7:50

Sure, the scale argument.

SPEAKER_00 7:51

Right. But isn't that massive pool, that wide net, exactly what a company wants? If they miss a few great people but fill the seat cheaply, hasn't the system worked?

SPEAKER_02 8:01

That is exactly the justification executives use. But the source material pushes back hard on this. It ignores the principal agent problem at the heart of modern human resources.

SPEAKER_00 8:12

Walk us through that. How does that work here?

SPEAKER_02 8:14

So the principal agent problem happens when the person making a decision has different incentives than the person who actually bears the consequence of that decision.

SPEAKER_00 8:21

Okay, so the recruiter versus the manager.

SPEAKER_02 8:23

Exactly. The recruiting department is the agent. They are incentivized by metrics like time to fill, cost per hire, pipeline volume.

SPEAKER_00 8:30

Their goal is just to close the ticket quickly.

SPEAKER_02 8:32

Right. But the engineering manager, the principal they have to actually work with the person hired, they bear the long-term cost if the hire lacks curiosity or adaptability or true problem-solving skills.

SPEAKER_00 8:45

So the ATS optimizes for the recruiter's metrics, but actively destroys the manager's ability to find the best talent.

SPEAKER_02 8:52

Bingo. And the source material points out something even crazier. The volume that recruiters complain about, that influx of hundreds of applications.

SPEAKER_00 9:01

It's self-inflicted.

SPEAKER_02 9:02

Entirely self-inflicted. Generic job descriptions create the bloat. They read like shopping lists of buzzwords.

SPEAKER_00 9:08

As you know, the bingo cards. Agile, AI, leadership, cloud, DevOps.

SPEAKER_02 9:14

Right. And thousands of people look at that and think, yeah, I vaguely match that. So they apply.

SPEAKER_00 9:18

And the author brings up specific technical hurdles too, like demanding someone have DevOps experience and a Safe Six certification.

SPEAKER_02 9:25

Which is wild if you know what those are.

SPEAKER_00 9:27

Exactly. For those outside the engineering bubble, DevOps is basically this culture where people writing the software also manage the servers it runs on. It's very broad.

SPEAKER_02 9:36

Very adaptable.

SPEAKER_00 9:36

Yeah. And SafeSix is this very specific, incredibly rigid corporate framework for managing large teams.

SPEAKER_02 9:44

So when you pile those distinct, sometimes contradictory requirements into one job post, the truly talented people look at it and realize the company doesn't actually know what it wants. They opt out. But the average candidates look at the vague buzzwords, figure they match leadership, and throw the resume in.

SPEAKER_00 10:00

The author has this phenomenal rule for this. Precision attracts precision.

SPEAKER_02 10:05

I love that line.

SPEAKER_00 10:06

It's so good. A well-written dub description should make half the readers immediately think, oh, this is not for me.

SPEAKER_02 10:13

Yes, you want people to opt out. But instead, companies dilute the work and try to market a lifestyle.

SPEAKER_00 10:19

Oh, the lifestyle ads. It's ridiculous. The endless list of perks, free lunch, unlimited PTO, wellness budgets. Ping pong tables. Right. They spend more word count on that than the actual problems the company is trying to solve.

SPEAKER_02 10:31

Which completely alters the psychology of the applicant pool. You attract candidates who only care about what's in it for me.

SPEAKER_00 10:38

Rather than looking at the challenge.

SPEAKER_02 10:39

Exactly. And that inflates your pipeline with lifestyle seekers, forcing the recruiter to rely even heavier on the ATS. It's a self-perpetuating cycle.

SPEAKER_00 10:49

So naturally, with all these generic applications flooding in, companies turn to even more technology to save them. The big trend now is assuming AI will fix it.

SPEAKER_02 10:58

Oh, the AI myth. People think modern ATS will detect patterns beyond keywords.

SPEAKER_00 11:04

Right. They think it'll magically find the hidden gems.

SPEAKER_02 11:07

But the flaw is foundational. If the input is generic buzzwords, the output is generic matching. AI cannot read between the lines.

SPEAKER_00 11:15

It can't detect humor or originality.

SPEAKER_02 11:17

Or sincere curiosity. It's just highly sophisticated string matching, converting text to mathematical vectors.

SPEAKER_00 11:24

Here's where it gets really interesting, though. The author asks hiring managers this brilliant question. Are you looking for someone with exactly Python 6.7 and Safe 6 certification?

SPEAKER_02 11:34

Or are you looking for a force of nature?

SPEAKER_00 11:36

A force of nature.

SPEAKER_02 11:37

It's such a vital concept. We're talking about someone who learns aggressively, adapts to shifting markets.

SPEAKER_00 11:42

Someone who will just pick up whatever specific tool you need within a few weeks anyway.

SPEAKER_02 11:45

Exactly. But the tragedy is the force of nature candidate is the exact profile the algorithm is mathematically designed to reject.

SPEAKER_00 11:53

Because their resume looks messy to a machine. Maybe they jumped from finance to open source or did an unconventional startup.

SPEAKER_02 12:00

They don't fit the square boxes.

SPEAKER_00 12:01

Right. And the author makes this hilarious point. If matching people based on data co files was easy, we would all have found our better half on Tinder.

SPEAKER_02 12:10

Human chemistry, professional or romantic, just cannot be reduced to a checklist. Which brings us to talent versus specialization.

SPEAKER_00 12:24

Let's define that. Companies get this twisted constantly.

SPEAKER_02 12:27

If you need a specialist for a highly specific, mathematically bounded problem, then be precise. But don't hunt for a unicorn by attacking on front-end, back end, cloud, and DevOps to one role.

SPEAKER_00 12:39

Right. If you want talent, you have to look outside LinkedIn.

SPEAKER_02 12:41

You look in networks, open source communities, conferences.

SPEAKER_00 12:44

You write an ad that speaks to their intellect, which forces us to look at the darkest consequence of this entire system.

SPEAKER_02 12:50

The psychological pull.

SPEAKER_00 12:52

Yes. We've talked about the managers, but what about the talent navigating this broken pipeline? If you're listening and you've applied for a job recently, you know this pain.

SPEAKER_02 13:02

It's brutal.

SPEAKER_00 13:04

A candidate spends four to eight hours tailoring a CV, writing a cover letter.

SPEAKER_02 13:08

Making a massive investment of hope.

SPEAKER_00 13:10

And then waiting, just checking the inbox, until two weeks later you get the automated rejection.

SPEAKER_02 13:16

The no reply email. Too many applications to give feedback.

SPEAKER_00 13:20

Which is draining. But then comes the ultimate slap in the face. A week later, an automated email arrives asking the rejected candidate to fill out a survey.

SPEAKER_02 13:29

Giving their feedback on the recruitment process.

SPEAKER_00 13:31

Yes. After getting no feedback themselves.

SPEAKER_02 13:33

If we connect this to the bigger picture, companies think they are optimizing for efficiency here, but they are actively destroying their own brand. Totally. They alienate hundreds of applicants who read that rejection as thank you for wasting your time. We are too busy to care.

SPEAKER_00 13:49

And those people will never apply again.

SPEAKER_02 13:51

Never.

SPEAKER_00 13:52

So what does this all mean? We know the system is broken and alienating, so how does the listener, especially if they are or will be a manager, actually fix this?

SPEAKER_02 14:02

Well, the author provides a very specific playbook for managers to take back control. First, write ads that exclude people.

SPEAKER_00 14:10

Be painfully honest.

SPEAKER_02 14:11

Yes. Stop marketing a lifestyle. Second, do not outsource your judgment to an ATS.

SPEAKER_00 14:18

Use it to schedule invites, sure, but don't let it dictate the shortlist.

SPEAKER_02 14:22

Exactly. Third, give feedback to candidates. It builds your reputation in the market.

SPEAKER_00 14:27

Just treating people like human beings.

SPEAKER_02 14:28

Right. And finally, shortlist a bit too broadly, then do 30-minute calls to assess how they think, not just what they've done.

SPEAKER_00 14:35

Present a vague problem and see how they react when they don't know the answer.

SPEAKER_02 14:39

Do they get defensive or curious? The author summarizes it perfectly. Experience tells you what someone has done, thinking tells you what they will be able to do.

SPEAKER_00 14:47

I love that. You're hiring for the future. And it just reminds us that while technology is great for structuring a process, choosing people is fundamentally a human responsibility.

SPEAKER_02 14:57

We cannot automate judgment.

SPEAKER_00 14:58

We really can't.

SPEAKER_02 14:59

And it leaves us with this final thought to mull over. If our modern hiring systems are perfectly optimized to filter out unconventional thinkers and forces of nature, what happens to the long-term innovation of an entire industry?

SPEAKER_01 15:13

Wow.

SPEAKER_02 15:14

When the only people who get hired are those who perfectly mimic the buzzwords, are we inadvertently automating mediocrity?

SPEAKER_00 15:22

Automating mediocrity. That is a heavy but incredibly necessary question to ask. If we don't fix this, we're all just going to keep dropping coins down a digital well. Thank you so much for joining us on this deep dive. Keep questioning the systems around you, and we'll catch you next time.

SPEAKER_01 15:47

Thank you for listening to the Agile Engineering Deep Dive Podcast. If you found this episode valuable, feel free to share it with someone who might benefit. A colleague, your team, or your network. You can access all episodes by subscribing to the podcast and find their written counterparts in the Agile Software Engineering newsletter on LinkedIn. And if you have thoughts, ideas, or stories from your own engineering journey, I'd love to hear from you. Your input helps shape what we explore next. Thanks again for tuning in, and see you in the next episode.