When we talk about mass layoffs, especially in tech, we tend to focus on the wrong numbers.
I’ll give you an example.
From this CNBC article that appeared over the weekend, about a Durham, North Carolina (close to home, Go Bulls!), technical program/project manager now six months unemployed:
Starting around December 2025, when he was laid off from his previous role, [Dan] Coda says he spent more than 300 hours job hunting, submitting dozens of applications, hearing back about 15 potential roles and going through multiple rounds of interviews to no avail.
Those are some depressing numbers. So Coda gave up. In fact, the article is titled “Why People Are Dropping Out of the Workforce and Not Looking for New Jobs: ‘The Market Wore Me Down.’”
Yeah, it’s been doing that.
But that’s only a part of a larger problem.
The numbers tell a scary story
Now, I absolutely empathize with Mr. Coda. But in my now-years-long deep dive into the problems in the tech industry, its labor market, and its hiring practices, I can tell you some stories.
Three hundred hours job hunting? I’ve heard “thousands” of hours, from hundreds of laid-off folks.
Dozens of applications? I’ve seen the file dumps of hundreds of individual tailored applications.
Fifteen callbacks? That’s actually above the norm. It’s usually about one a month, if you’re lucky.
Multiple rounds of interviews? How about 10 rounds for one job?
Look, I don’t mean to undersell Mr. Coda’s experience. I don’t know him, and in fact I hope to give him and folks like him a little extra pub on top of the CNBC pub. I just want to use his numbers, compared with the numbers coming out of my network over the past two years, to show it’s even worse than what CNBC just published.
And still, those aren’t even the numbers we should be worried about, because there are bigger, scarier numbers that have been with us in tech for over two decades and no one really talks about with any urgency.
The scarier numbers
While the focus on tech mass layoffs is usually on the empathy-driving individual stories, which now have numbers to quantify their individual struggles, it’s the aggregate that worries me.
The article rightly goes on to connect Mr. Coda’s predicament to the larger story of the mass layoff hysteria over the past two years. We’re hitting unemployment numbers not seen since the Great Recession, both in the number of workers laid off and the number of long-term unemployed. Yet the unemployment rate remains relatively low.
From the CNBC article:
In June, more than 1.9 million Americans, or roughly 1 in 4 unemployed people, were jobless but still looking for work for longer than six months, according to U.S. Bureau of Labor Statistics data.
There’s a reason all these things are true. One big reason.
The big fat scary number
Those of us who do remember the dark times of the Great Recession (2008-2009), and before that, the dot-bomb era at the turn of the millennium, we might also remember that the recoveries that followed seemed a little more hollow than your standard tech job recovery.
Eventually, markets recovered, employment rates recovered, hiring rates recovered, and salaries recovered, just like they should. But it just didn’t seem like the job market was as robust as it once was. And that’s because, in each of those cases, it took an extra couple of years for the Labor Participation Rate — the number of people actively working or looking for a job — to recover.
And over 26 years now, especially in tech, it hasn’t ever fully recovered. And we’re not done. From the CNBC article:
A continuous low-hire, low-fire job market could make it more challenging to find work and push more people out of the workforce altogether.
We’ve been in low-hire mode for years, and I don’t think we’ve reached low-fire just yet. Optimism, tho.
How AI fans the flames of falling labor participation
The thing about this particular mass layoff cycle that separates it from previous cycles is the often misguided tendency of those doing the firing to blame AI for all the carnage. Whether true or not (it’s not), tech workers have heard the message loud and clear:
Oh, really?
With that message received, the experienced oldsters in tech are leaving the tech workforce, at least the ones who have the means to do so. And thanks to the windfall cycle of tech, a lot of them have those means, either by retiring early, doing their own thing, or changing their career path completely and downsizing.
The industry is already starting to need a lot of them back. Furthermore, we haven’t replenished their ranks.
From the article:
June saw the biggest one-month drop in prime age labor force participation of workers ages 25 to 54, excluding the Covid-19 pandemic, since June 1976. Roughly 720,000 people stopped working or looking for work between May and June, according to the St. Louis Fed. Those leaving the workforce are both men and women, and the majority are ages 25 to 34, says Heather Long, chief economist at Navy Federal Credit Union, who specializes in the economics of the middle class.
This is a problem. It’s the problem. The big problem.
Keep an eye on labor participation
It’s nearly impossible and thus ill-advised for a single company to try to respond proactively to macro-economic downward shifts. It’s better to play defense. So in this case, I don’t have an answer, just a distant early warning.
Labor participation needs to be a leading indicator, not an afterthought, especially in tech and other forward-facing industries, because the downstream effects cascade through everything else, in terms of who is buying what and how much.
And while that number is at 1976 levels now, there’s room to run and the bottom could still fall out. If that happens, I think we see major downstream effects in inflation, purchasing, consumption, and eventually GDP growth.
If labor participation doesn’t recover soon, I’d be planning for business customers to continue to want more for less. AI has promised those customers exactly that, so if you’re not already thinking about how to deliver on that promise, that big fat scary number is screaming at us that now is the time.
The companies that figure out how to deliver more, real value at lower cost, not by replacing people with AI, but by using AI to make their people more valuable, those companies are the ones best positioned to survive what’s coming.
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This article originally appeared on Fast Company’s sister website, Inc.com.
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