Posted on Jan 16

Why nobody wants to hire developers anymore (and what actually fixes it)

#webdev #ai #programming

The market didn’t get “more competitive.” it got more broken: ghost listings, ats bots, and roles that want a whole dev team in one hoodie. here’s how to get seen by humans again.

You ever notice how everyone keeps yelling “talent shortage!” while your inbox looks like a graveyard of auto-rejections?

Same energy as a game dev tweeting “matchmaking improved” right after you get dropped into a lobby with 900 sweats, 3 smurfs, and one guy who’s clearly a bot farming xp.

something changed. not your brain. not your ability to ship. the pipeline.

Job boards turned into a firehose. companies post roles that aren’t real roles (or aren’t real yet). recruiters drown in applications and slap a robot bouncer at the door. and somewhere in the middle, you’re sitting there like: i can literally build this product. why am i losing to silence?

And the funniest part? half the “requirements” now read like a cursed DLC pack: backend + frontend + devops + “ai experience” + “ability to thrive in ambiguity.”

TL;DR: you’re not getting ignored because you’re useless. you’re getting filtered because hiring became a risk-minimization game. the good news is: once you understand the new rules, you can stop playing “apply harder” and start playing “be impossible to ignore.”

The market got weird (ghost jobs + application spam)

The first thing to accept is kinda annoying: a lot of the silence you’re reading as “i’m not good enough” is actually “this job wasn’t real, wasn’t urgent, or got buried under a landslide.”

“ghost jobs” aren’t some conspiracy theory anymore. even mainstream reporting has been calling out listings that stay up without real intent to hire, or postings kept warm to collect resumes and look busy. Wall Street once you mix that with one-click applying, you get the worst combo: more listings that don’t convert + more applicants per listing.

Linkedin has pointed out that the applicant-per-open-job pressure jumped compared to a couple years ago (think: the lobby got more crowded even if you didn’t get worse). LinkedIn so recruiters respond like any overwhelmed system responds: rate limiting. auto-filters. template rejections. or just… nothing.

I learned this the dumb way: i applied to a role that matched my exact stack, my exact scars, my exact “yes i’ve been paged for this” experience. got rejected so fast it felt like the email was pre-rendered. later i noticed the posting had been reposted for weeks like an npc quest marker that never completes. classic.

Takeaway: stop treating the job board response as a pure skill signal. half the time it’s just noisy telemetry from a broken pipeline.

The robot bouncer (ats + filters)

Most companies aren’t rejecting you. they’re rejecting your pdf’s ability to survive a text parser.

An applicant tracking system (ats) is basically a giant inbox and database where recruiters can parse resumes into fields and then search/filter candidates fast. lever’s own docs straight-up describe resume parsing as extracting your info into the candidate profile. that’s not “judging your potential,” that’s “turning your resume into structured data.” Lever Help Center

And once your resume is in that database, recruiters can do the most human thing imaginable: ctrl+f your life. greenhouse even documents “search resumes for keywords” as a built-in feature. (greenhouse keyword search)

So yeah, if your resume says “k8s” and the job description says “kubernetes”, you can lose the match like you failed a type check. i’ve seen this happen. it feels personal, but it’s literally string matching plus recruiter search habits.

What fixes it (without turning your resume into keyword soup):

Write the full term once, then the shorthand: “kubernetes (k8s)”
Mirror the core nouns from the job post (tools, role title, domain)
Keep formatting simple so parsers don’t faceplant (tables/columns can break parsing)

The new baseline: AI + full-stack-everything

Here’s the sneaky part: “AI” is starting to behave like “git.” it’s not always in the job description because a lot of managers already assume you’ll use it. there’s reporting pointing out that even when listings mention AI less, employer expectations around ai fluency are still high like it’s becoming basic workplace hygiene.

And recruiters are leaning into this “skills-based” thing hard. linkedin’s recruiting reports are basically waving a flag saying: we’re shifting from pedigree to skills signals, and ai is becoming part of how those skills are discovered and validated. LinkedIn Business that sounds nice on paper, but in practice it means you get tested on stuff that used to be optional: can you break down a messy problem, use tools responsibly, and prove you didn’t just paste a magical blob into prod.

Also: teams got flatter. responsibilities didn’t disappear. they just got duct-taped onto whoever is still standing. so “backend developer” quietly becomes “backend + infra + a little frontend + also please know security + also can you set up observability” like a cursed character build where every stat is 7 and none are 20.

The world economic forum even lists “AI and big data” among the fastest-growing skills, alongside broader “technology literacy.” translation: you don’t have to be an ml researcher, but you can’t act surprised that ai exists.

Takeaway: don’t try to look like an ai wizard. look like a responsible operator: you can use ai, verify outputs, write tests, explain tradeoffs, and ship without setting the repo on fire.

Why interviews feel harder now (risk, signal, trust)

Here’s the part nobody says out loud: hiring isn’t a “talent search” anymore. it’s risk management with a calendar invite.

because a bad hire is expensive in boring, spreadsheet-y ways. SHRM benchmarking puts average cost-per-hire at nearly $4,700, and that’s before you count all the hidden damage like time lost, slower delivery, and the team doing the “carry this person” side quest. SHRM’s own research notes many employers estimate total hiring costs can be three to four times the position’s salary, and they cite managers spending serious time coaching when a hire isn’t working out.

So when a company runs an interview loop that feels like a hazing ritual, it’s not always because they want to be mean. it’s because they’re trying to answer one question: “is this person a predictable win, or a risky unknown?” and in a world where teams are smaller and deadlines are tighter, “unknown” gets treated like a production incident.

That’s why you see interviews shift toward stuff that proves signal fast: clearer communication, tradeoffs, debugging under pressure, writing things you can maintain, not just “can you invert a binary tree while sleep-deprived.”

Don’t optimize for sounding impressive. optimize for sounding

Trustable: crisp stories, concrete outcomes, and proof you can ship without lighting the repo on fire.

The proof-of-work playbook (how to route around the funnel)

If the hiring funnel is a broken matchmaking system, your job is to stop queueing solo and start showing gameplay footage.

Because resumes are claims. and hiring right now is obsessed with one question: “can i trust this person to ship without turning prod into a campfire?” the fastest way to answer that is proof-of-work: a small, real thing you built, deployed, documented, and can explain like a normal human.

here’s the playbook that actually moves needles:

1) Ship one “boring but real” project
Not “my next big saas.” I mean: you need to integrate an API, a database, authentication, caching, and basic observability. pick a scope you can finish. then deploy it and write down what broke. if you need project ideas that aren’t fluff, steal one from build-your-own-x and implement a mini version you can demo.

2) Write a README like you’re onboarding your future teammate
Include: what it does, how to run it, architecture diagram, tradeoffs, and what you’d do next. if you’re on aws, use the aws well-architected framework as your cheat sheet for “why did you design it like that?” (security, reliability, cost, ops, performance).

3) Show judgment, not buzzwords
A recruiter hears “kubernetes” 500 times a day. what they don’t see often is: “i used k8s because X, here’s what i’d do differently if traffic doubled.” link to the actual k8s docs you followed, and keep it honest.

4) Make your profile easy to verify in 20 seconds
Pin 2 repos. add a short “start here” note. and if you’re unsure what “good” looks like for your niche, browse an awesome list and pick one standard tool to learn properly instead of five half-learned ones.

Conclusion (what’s next for devs)

I don’t think “nobody wants to hire you.” I think the hiring pipeline got optimized for the wrong metric: reduce risk fast, even if it accidentally filters out good people.

Ghost listings and “we’re hiring… kinda” posts make the board look healthier than it is. Wall Street Journal ats tools turn your resume into searchable text, and recruiters literally keyword-search it like it’s a log file. Lever Help Center meanwhile companies are paying real money per hire and they feel that risk, so the interview loop gets stricter and the “ready-now” bar goes up. SHRM and on top of that, ai fluency is quietly becoming assumed baseline even when it’s not spelled out.

So the play isn’t “apply harder.” it’s signal clearer: ship small things, document tradeoffs, and make it easy for a human to trust you in 20 seconds.