Spin Cycle: How Recessions Reshape Talent Acquisition Strategies

Remember 2021? When a carton of eggs cost $1.27, the Weeknd was still relevant enough to land the Super Bowl halftime show, X was called Twitter, people under 50 still used Facebook and Q was still making drops? I know, it seems like a fever dream to me, too – those halcyon days of headcount hyperscaling.

Back then, signing bonuses outpaced salaries, every recruiter had “hypergrowth” in their LinkedIn headline, a technical sourcer with a couple years of experience could pull in close to 200k at the FAANG company of their choice, and HR Tech vendors were busy convincing us that their new AI solutions were going to end candidate ghosting and improve the candidate experience?

Good times.

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Analytics Anonymous: The 7 Steps to Talent Intelligence Transformation

Let’s talk about the real reason your hiring strategy isn’t working.

It’s not a talent shortage. It’s not hiring manager alignment. And it’s definitely not because your employer brand video doesn’t autoplay on mobile.

It’s because you have no idea what’s actually happening in your funnel, and worse, you’ve built an entire process on data you don’t even trust (or in many cases, even capture).

According to the Deloitte 2024 Global Human Capital Trends report, only 9% of HR leaders say they are “very confident” in the accuracy of their workforce data. Just let that sink in. That means fully 91% of people teams are making hiring decisions based on a mix of outdated reports, disconnected systems, and vibes. This is absolutely asinine.

And it gets worse.

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Man Versus Machine: The Truth About AI In Interviewing

There was a time when interviews were about two things: figuring out if someone could do the job, and whether you could stand to share a Slack channel with them. Now? 

Now, our evaluation criteria has become a bit more esoteric: things like whether they blink too much, smile too little, or fall outside a statistical model trained on “proprietary data” that’s almost certainly pirated intellectual property.

Welcome to hiring in the age of the algorithm, where we’ve replaced gut instinct and basic decency with webcam recordings, acoustic patterning, and chatbots who “just want to get to know you better.” 

Which is only kind of creepy. 

Nobody asked for this. Especially not candidates. And definitely not hiring managers, who actually enjoy the one part of recruiting that doesn’t involve updating the ATS while pretending your HRBP is going to read the notes.

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When Dinosaurs Ruled: Why the OpenAI Jobs Platform Could Be An HR Tech Extinction Event

It’s fitting, really, that the beginning of the end for LinkedIn came with the arrival of an existential threat whose biggest investor also happens to be their outright owner, Microsoft – which is probably pretty awkward for a company whose post acquisition autonomy has created what turns out to be a pretty significant blind spot in their product roadmap.

In a poetic bit of corporate cannibalism, OpenAI (the ChatGPT people) announced this week that it’s building an “AI-powered hiring platform.” 

Not a tool. Not a plug-in. 

A full-stack hiring marketplace with matching, screening, certification, and employer tools built directly into the OpenAI ecosystem. 

It’s called the OpenAI Jobs Platform. And it’s coming for LinkedIn’s lunch, dinner, and market share.

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LinkedIn Hiring Assistant: Clippy Makes A Comeback

Somewhere along the line, the people who build HR tech stopped believing in recruiters. You can see it in every product launch dressed up as a breakthrough. 

Every time a new AI “assistant” emerges, armed with autocomplete and the thinnest veil of generative AI, the implication isn’t subtle. It’s that recruiters are inefficient, inconsistent, and expendable. 

That the problem isn’t a broken hiring system, it’s the humans still foolish enough to try to fix it. That, maybe, we’d all be better off if someone – er, something, just took over.

Which brings us to LinkedIn’s Hiring Assistant, which just expanded its availability globally after a year in pilot, just about long enough to grab some training data, create some customer success stories, generate some outcome data and package it all up in a product marketing bow.

It’s being framed as a milestone in recruiting automation. In practice, it’s more like Clippy got a makeover, learned how to parse job descriptions, and started sending templated InMails at scale.

It’s just as obnoxious as it sounds.

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