Not that long ago, posting a job opening for a tech role would bring in 20 to 50 applications. Most of them —more or less— relevant. Today, the same job post might get 500 applications in just 24 hours.
And it’s not because there’s suddenly five times more talent available.
What we’re seeing is a flood of résumés. (View Highlight)
Hays reported last year (2024) that 40% of candidates had already used AI to build their résumé. And they’re predicting that, within five years, 80% of all CVs will be AI-generated. (View Highlight)
In this new landscape, posting a job on LinkedIn or generic job boards has become unreliable at best. Companies are flooded with hundreds of applications — many of them completely off-target. Meanwhile, genuinely talented candidates get lost in the noise. (View Highlight)
AI was supposed to help us save time and boost our chances of landing better jobs. And to be fair, it is doing that — to an extent. But it’s also creating some weird side effects: candidates who no longer write their résumés at all, but generate a new one for every job posting. Or worse: they hand over their job search to bots entirely. (View Highlight)
These days, all it takes is pasting a job description into ChatGPT and asking it to craft a résumé that fits like a glove. The result? Hundreds of résumés that look virtually identical. Same structure. Same buzzwords. Tailored to match the job ad perfectly — but with zero real context or experience behind them. (View Highlight)
This has created a paradox: the tools that promised toset us apartare now making everyone look the same.
From the hiring side, it’s harder than ever to tell who’s genuinely interested, who actually fits the role — or even who’s read the job description before applying. (View Highlight)
And to be clear: using AI to help with your application isn’t necessarily a bad thing. It can be helpful — when it’s used thoughtfully. But what we’re seeing is the opposite: lazy, mass-produced, fully automated applications. Zero filter. Zero intention. Zero effort. Just shooting in the dark, at scale.
The consequences are already showing: overwhelmed recruiters, slower hiring processes, and rising frustration on both sides of the table. (View Highlight)
Companies are drowning in noise — struggling to find actual talent among the noise of AI-generated lookalikes. And legit candidates feel invisible, buried under a pile of clones.
Even skilled professionals with great experience are starting to suspect that it’s no longer enough to be good at your job. Now you also have to outsmart the algorithms, write like a prompt engineer, and compete with bots. (View Highlight)
It’s a vicious cycle: the less effective traditional applications become, the more people turn to automation. And the more automated things get, the less trust there is in the whole system.
For years, tech hiring was all about scarcity: too few people, too many jobs. Simple. But now? The game has changed. It’s not about how many applications you get — it’s about how many actuallymatter.
We’ve seen job ads drowning in hundreds of CVs within hours, even when the role is super specific — with clear requirements like “must have this experience” or “must live in this city.”
And the pattern? Always the same: CVs that look perfect on paper, but fall apart as soon as someone actually reads them. Candidates who don’t even know which company they’re applying to. People on the other side of the planet throwing their hat in the ring for on-site gigs, despite the listing spelling out loud and clear: no relocation, no visa support. (View Highlight)
This shift has created a perfect storm for recruiters. What used to take a couple of weeks now drags on for months — because sifting through a mountain of irrelevant profiles is a nightmare. And it’s not just slowing things down — it’s making recruiters lose faith in the whole system.
More and more companies are telling us: “Open job posts on LinkedIn? Not worth it anymore.” Why waste hours sorting through 500 CVs when 490 of them are just noise? Instead, they’re leaning on niche communities, trusted referrals, handpicked databases, or recruiters they actually know (View Highlight)
LinkedIn — once the holy grail of tech talent hunting — is losing its magic. Its superpower, reach, has become a double-edged sword. The more eyes on a job, the more noise it attracts in a world where hitting ‘apply’ costs zero effort.
So yeah, talent scarcity is still real — but now it’s buried behind a wall of automation, bots, and zero real intent. (View Highlight)
Meanwhile, candidates aren’t just using AI to write their CVs anymore. Now they’re using it to prep for interviews, generate avatar-based videos, or even complete technical tests in real time with the help of copilots and other tools. Some platforms are already building in countermeasures to detect this kind of trickery — but it’s a constant arms race.
The result? Processes that feel cold and mechanical. Decisions driven by opaque metrics. And candidate experiences that sometimes border on the surreal. Imagine talking to a camera, answering questions from an AI, with no clue if anyone — anyone — will ever watch the footage. (View Highlight)
What started as a fix for volume is now creating a new distortion. And we’re sliding into a world where neither side — company nor candidate — really knows who they’re talking to anymore. (View Highlight)
Recruiters across Silicon Valley are sounding the alarm.
Emi Chiba, HR Tech analyst at Gartner, put it plainly:
“The number of candidates using fake identities keeps growing. Without action, we could be looking at one in four applicants being fraudulent in just a few years.” (View Highlight)
This isn’t just a tech issue—it’s a legal and reputational minefield. Hiring someone who isn’t who they say they are can lead to data leaks, IP theft, compliance nightmares, and security breaches that make your infosec team break into cold sweats.
Traditional checks—like a quick skim of LinkedIn or a casual reference call—just don’t cut it anymore. (View Highlight)
Some companies are starting to fight back with biometric ID checks and third-party verification platforms. But these tools, while useful, can also backfire—introducing friction and suspicion into the process for legit candidates if handled poorly.
The bottom line? The floodgates are open. And if we keep running traditional hiring processes in a world where anyone can fake everything, the consequences could hit harder—and faster—than we think. (View Highlight)
Saturation, fraud, and automation-without-intent are pushing companies to rethink their whole talent strategy from the ground up. A few ideas are already on the table:
• Reduce exposure: post only in curated or trusted spaces —tech communities, vetted talent pools, strong referral networks.
• Flip the process: instead of opening the floodgates, go outbound. Be intentional about who you reach out to.
• Validate before getting excited: short take-home tasks, human screening calls, basic identity checks… before anyone wastes time (or hope).
• Evaluate context, not just keywords: read between the lines. Look for signal in the noise. Don’t get fooled by a CV polished by ChatGPT. (View Highlight)
This doesn’t mean going full artisanal with 6-week timelines. It means designing smarter flows. More conversational. More transparent. Where people know why they’re applying, and hiring teams know what they’re actually looking for.
Because if we keep running this as a bot-vs-bot arms race… The real talent —the ones worth fighting for— will be the ones paying the price. (View Highlight)
The rise of generative AI didn’t break the system —it just pulled the curtain back. It exposed what we already knew: that many hiring processes were inefficient, impersonal, and obsessed with volume over value. That both candidates and companies had started playing a game where everyone loses —more automation, less context; more CVs, fewer actual conversations. (View Highlight)
For years, posting a job ad was the default move when hiring. Today? More and more companies are thinking twice. Not because of a lack of candidates —but because of too much noise, too little signal, and a growing distrust in the system.
This isn’t a trend. This isn’t “just AI doing AI things”.
It’s the collapse of a model that ran out of steam. A model where the CV lost all credibility, the main channels got flooded, and the connection between companies and candidates got so blurry that you don’t even know if you’re talking to a person anymore.
Is this the end of public job offers in tech? Maybe —at least the way we used to know them. (View Highlight)
Automation with n8n to get a curated selection of job offers that match you, straight to your inbox – by Pau Ramon. (View Highlight)
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