When Everyone Rehires at Once

This week's edition was written by Graham Thornton, President of Consulting and Growth at Talivity.

Subscribe here to get this newsletter to your inbox.

Jenga has a specific cruelty. Every block you pull looks free. What you cannot see is the load shifting up and down the tower with every move. The structure gets weaker while it keeps standing. Then one ordinary pull and it is on the table all at once.

I heard that analogy on a podcast a few weeks back and it has been rattling around since. A lot of companies are playing Jenga with their org charts right now. Pull the routine tasks. Pull the entry-level roles. Pull the process nobody could defend in a team meeting. Every quarter the tower still stands, so every quarter the pulling looks smart.

CNBC reported this month on what happens when it stops working. Ford is rehiring hundreds of experienced engineers because automated systems could not hold vehicle quality. Commonwealth Bank of Australia replaced more than 40 service roles with a voice bot, watched call volume climb, and reversed the cuts. IBM automated roughly 94 percent of routine HR requests, then announced it would triple entry-level hiring. One survey found 55 percent of leaders who cut roles because of AI now call it a mistake.

Last week I wrote about what those cuts do to your own leadership bench. This week I want to pull on the other thread, the one I have not seen many people connect yet: what happens when everyone tries to rebuild at the same time.

These reversals will not arrive politely spaced out. The companies above all cut for the same reasons, on roughly the same timeline, chasing the same board-level ROI story. When the rehiring comes, it comes in a wave. Everyone bidding for the same people at once.

And they will be bidding into a smaller pool than the one they cut from. In June, 720,000 people left the US labor force in a single month. Participation fell to 61.5 percent, the lowest reading outside the pandemic since 1976. Laura Ullrich at Indeed Hiring Lab reads that as a supply story: boomer retirements and immigration policy changes are shrinking the workforce, and the projections say it keeps shrinking.

I shared that read on LinkedIn last week and got some fair pushback. We are in a low-hire, low-fire market, and commenters pointed out the June exits skewed young, heaviest among workers 25 to 34. That looks less like retirement and more like discouragement, people early in their careers giving up after months without traction. So supply tells part of the story, and I probably leaned on it harder than I should have. But the fuller picture is not more comforting for employers. A workforce thinning from the top through retirements and from the bottom through young people walking away is short at both ends. And the mid-level talent everyone thinned out is also the talent nobody else developed, because everyone made the same cut at the same time.

So the hiring market coming for these roles looks, at least to me, like the most competitive one in years. Fewer workers overall. More employers chasing them at once. No internal bench to promote from, because that was the first block pulled.

Which raises a question I do not think most TA leaders have connected to this yet: where will those candidates form their opinion of you?

Increasingly, inside an AI conversation. Indeed's research last year found 70 percent of job seekers already use generative AI to research companies. When a candidate asks ChatGPT or Gemini what it is like to work somewhere, whether the pay is fair, how the interviews go, something answers. In the diagnostics we run, the employer's own pages are usually the minority voice in that answer. Close to half of AI citations trace back to community platforms like Reddit and Glassdoor. Third parties are doing most of the talking about you, and they are not always kind.

Here is what makes it a now problem. That kind of visibility compounds. The structured content, the technical fixes, the pages a model can actually read and trust, all of it takes months to build and longer to get cited. The employers who walk into the rehiring wave with an advantage will be the ones who did that work while hiring was quiet and everyone else was staring at the cuts.

The Jenga lesson I keep coming back to goes beyond knowing which blocks in your own tower are load-bearing. Everyone is playing at the same table, on the same clock. When the towers start falling together, the scramble is collective, and the advantages that matter then are the slow-built kind. The kind you cannot buy in the quarter you suddenly need them.

I could be wrong on the timing. Maybe the reversals stay a trickle instead of a wave. But a shrinking labor supply is not a maybe, and neither is where candidates do their research now. Preparing for a competitive market you might face seems better than explaining, two years from now, why an AI told your best candidates to go work somewhere else.

What I'm Reading

Employers Who Laid Off Workers Citing AI Are Already Starting to Regret It – CNBC. The piece behind this week's letter. Ford, Commonwealth Bank, and IBM all walking back AI-driven cuts, plus the Orgvue finding that 55 percent of leaders who made them now say they got it wrong. Worth reading for the pattern: the cuts were spreadsheet-clean and the reversals were not.

Labor Force Participation Falls to 61.5%, the Lowest in 50 Years Outside COVID – Fortune. The piece I shared on LinkedIn last week. Most takes framed June's 720,000-person exodus as workers checking out. Laura Ullrich of Indeed Hiring Lab reads it as supply: the labor force is on track to actually shrink starting this year, driven by boomer retirements and immigration policy changes. The comments on my post pushed back on the demand side, and fairly. Either way the pool gets smaller.

We Analyzed 25,000+ AI Citations Across 5 Engines – DeltaV Digital. The study behind a post I shared this week. One B2B services firm's own website did not crack the top 30 domains AI cites about its own category, while a university in the same study saw 75 percent of its citations point to pages on its own domain. Most brands assume weak AI visibility means the model does not rate them. Usually it just never reads their site.

Connect with Author: LinkedIn | grahamt@talivity.com