Everyone's blaming AI for the layoffs. The data says something else is going on.
Fernando Olivares
Dot Fer | ProductFitCoach
Jul 20th, 2026
Your CEO isn't scared of AI. He's scared of explaining why the company needs fewer people than it did three years ago.
Close to 186,000 tech jobs have been cut globally in 2026. AI has been the number one stated reason for four straight months in a row. That streak has never happened before.
Here's what doesn't add up. The companies cutting the deepest are posting record revenue. Meta cut 8,000 people in May and raised its AI spending guidance to 115 billion dollars in the same breath. Google Cloud grew 63% and still quietly cut a third of its team managers.
So which is it? Is AI actually taking these jobs, or are we watching years of cheap money hiring finally get corrected, with AI as the headline that plays better than admitting the second one?
Both. And the mix matters more than the headline.
Where generative AI actually sits on the technology adoption curve in 2026.
Is AI Really Causing These Layoffs, Or Is It Something Else?
A Resume.org survey of 1,000 hiring managers found that 59% admit they lean on AI as the reason for layoffs because it plays better with investors than the truth. The truth, in a lot of cases, is simpler and less flattering. Four years of cheap money hiring, now getting corrected.
Even the people building this technology are saying it out loud. Sam Altman calls it AI washing. Jack Dorsey wrote in a memo last year that Block's cuts had nothing to do with AI. Eleven months later he attributed 4,000 more cuts directly to it. Same pressure underneath. Different headline on top.
A 2025 NBER study followed 25,000 workers and found zero measurable effect on earnings or hours from actual AI adoption. In any occupation. Including the ones seeing the steepest hiring declines.
AI is real. It is changing how specific tasks get done. But a good chunk of what lands in your feed as AI taking the job is really we overhired in 2021, and this headline plays better than admitting it.
If You're Reading This From Europe
It's worth saying plainly: most of what's in that 186,000 number is American. SAP cut 1,200 roles in Germany, Spotify trimmed its editorial team by 30%, but Europe's 2026 total sits in the low thousands, a rounding error next to the roughly 150,000 cut in the US alone. Some research this year even found European companies adopting AI are, on balance, still hiring. That doesn't mean the pressure isn't real here too. Junior tech roles across major EU markets fell by roughly a third in 2024, similar to the UK. It's just not the mass layoff story dominating your feed. That story is largely American.
So Where Is This Actually Heading? My Honest Read
Here's my personal take on this. I don't think we're close to humans and AI working together in a way that's genuinely lucrative and sustainable yet. I think that's coming. I just don't think we're there yet. The gap is smaller than the hype made it look, but bigger than the layoff headlines want you to believe.
Here's the part I think gets missed. It's not that the technology isn't good enough. It's that organizations aren't ready. Most of the talent already is. Most tech professionals I talk to already know how to work with these tools better than the processes around them allow. Right now, the bottleneck is organizational, not human.
The data backs this up
Though not in the way most headlines use it. MIT's Project NANDA studied enterprise AI pilots and found 95% show no measurable financial return. The reason isn't bad models, but integration. Tools got bolted onto existing workflows instead of built into how people actually work. That's an organizational failure, not a technology one, or a talent one.
The data backs this up
Gartner's 2026 Hype Cycle tells the same story from a different angle. Generative AI is sitting in what it calls the Trough of Disillusionment, the stage where the hype has faded and companies are slowly figuring out how to use what they already bought.
And here's a question nobody answers. If AI genuinely doubled a team's output:
Isn't the smart move to keep the people who already know the product, the customers, and how the company actually works, and train them on the tools?
That combination compounds. A trained employee plus AI should outproduce a new hire plus AI, because half of what makes AI useful is knowing exactly where to point it.
Instead, companies do the opposite. Cut the people who hold that knowledge, then spend months and real budget rebuilding it in someone new, who then has to learn the tools from scratch too.
That's not what you do when a technology is multiplying output. That's what you do when you need to shrink a cost line and AI is sitting right there as the explanation.
Record revenue, rising AI investment, and a shrinking headcount, all in the same slide. Generic illustration of what tech is living in right now.
So when does this settle?
Gartner projected that by 2026, over 80% of enterprises would have generative AI running in some form of production. By 2028, that climbs above 95%.
In other words, almost everyone already has the tools live. What's missing is the payoff. Most of what's been deployed still isn't producing results anyone can point to.
Line that adoption curve up with the usual three to five year run from hype to plateau, and my honest read is somewhere between 2028 and 2030 is when "live" finally starts turning into "working."
Two, maybe four years out. Not never. Not tomorrow either.
The uncomfortable part is we're the ones paying for that gap right now. Organizations are experimenting on our time, our headcount, and our stability while they figure out what works.
What Should You Actually Do About It?
If you got laid off, you're competing in the toughest tech job market in years. If you stayed, you're doing your job plus the job of whoever left.
That's real, and I'm not going to pretend otherwise.
But tighter and over are different words. Two things are worth doing right now, and most people end up doing a mix of both.
1. Build something with a real story behind it.
Not a side project for its own sake. Selling through AI requires substance, that thing only a human can bring. Solve an actual problem, and give your human touch. Something your can describe with specifics and through lived experience instead of vibes.
2. Make your work visible.
Hiring has shifted toward smaller companies and referrals while the big company ladder stays frozen. Being known for something is a distribution channel for opportunity, and it matters more in a tight market, not less.
I can't hand you a job. I'm suspicious of anyone in this market who tells you they can. What I can tell you is this. The people who come out ahead of an adoption curve like this one are rarely the ones who panicked hardest at the bottom of it.
The Part You Actually Control
The people who make it through this in one piece won't necessarily be the smartest.
They'll be the ones whose energy, focus, and health let them keep performing while everyone else burns out trying to survive the uncertainty.
That means sleep on a fixed schedule. Treat your job search or your project like the job it is. Cut back on the drink. Get bloodwork done if you haven't in a while. And move your body consistently.
I go deeper on the mechanics of these habits in 6 Daily Habits to Stay Productive and Healthy in the AI Era.
The Bottom Line
We're not living the AI jobs apocalypse the headlines are selling. We're in the messy middle of an adoption curve. Some companies overspending. Some quietly correcting years of overhiring. A few actually figuring out how to make the tools work.
That middle stretch usually lasts three to five years. The people who come out ahead are the ones who kept building and kept themselves intact while everyone else panicked at the bottom. That's the part I work on with tech professionals.
If you work in tech and your energy, focus, or performance aren't where you'd like them to be, here's how I can help:
Are you a founder or senior tech leader who wants to improve your health while getting in shape, increasing your energy and focus? Let's speak! Book a Free Strategy Call with me here.
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References
Challapally A, et al. The GenAI Divide: State of AI in Business 2025. MIT Project NANDA, 2025.
TechTimes, AI Leads US Job Cuts for Record 4th Month, July 2026.
Stanford Review, The Class of 2026 is Struggling to Find Jobs, and It's Not Because of AI.
Fortune, European Companies Using AI Are Hiring More Workers, Not Cutting Them, March 2026.