- AI & Tech
AI Didn't Cause Every Layoff. Management Did.
We’re living through one of the strangest periods in the history of technology, and honestly, I’m not sure we’re talking about it the right way.
Companies are pouring billions into AI. Executives keep announcing “unprecedented productivity” like it’s a magic number. Investors reward anything that smells like AI-driven efficiency. And in the same breath, thousands of people are being told their position has been eliminated.
The obvious villain here is AI. It can write code, summarize documents, answer customers, draft marketing copy, analyze spreadsheets. Work that used to take a whole team now takes a tool and a prompt. So the story writes itself: AI is taking our jobs.
But I’ve been around long enough to be suspicious of clean narratives. I’ve watched companies make bad calls for decades, long before anyone ever heard of a transformer. And my honest read is that AI gets blamed for a lot of layoffs it didn’t cause.
Sometimes AI genuinely replaces work. Sometimes it creates new work. And sometimes it’s just the most convenient excuse for a decision that was already coming.
That distinction matters. Because if we blame everything on AI, we’ll miss the real problem entirely.
The good
Let’s start with the uncomfortable truth. AI is genuinely powerful. I use it every day.
A developer can now explore an unfamiliar codebase, generate tests, chase down errors and document half a project in an afternoon. A marketing team can ship drafts ten times faster. A support team can deflect the questions that used to eat entire shifts. An analyst can chew through hours of manual work in minutes. A two-person startup can do what used to require twenty people.
That part is real. The International Labour Organization’s 2026 review of the evidence concluded that productivity gains from generative AI are real, though uneven and often stubbornly hard to translate into measured output, earnings or employment.
Here’s where I think leadership gets to make a choice. The question can be “how many people can we remove?” or it can be “what can our existing people accomplish now that they have better tools?”
Those are two very different management philosophies. And they lead to very different companies.
The bad
Now the part that worries me.
Say you run a company with a thousand employees. You discover AI might make people 30% more productive. The quick arithmetic goes something like: “well, we could probably run this place with 700 people.” So 300 people get cut, and the company announces “AI-driven transformation.” Costs drop, the stock bumps, the board loves the story.
But here’s the thing nobody wants to say out loud: AI does not redesign your organization. Removing people does not create productivity. It just removes people.
You still have your tangled processes, your technical debt, your meetings about meetings, your duplicated systems, your layers of management whose job is forwarding emails, your bad product calls. AI doesn’t fix any of that. In some cases, layoffs just remove the only people left who understood why the whole dysfunctional system worked the way it did. Then it collapses properly, and nobody can explain what happened.
The ugly
Here’s the part I think deserves a lot more conversation.
Not every layoff blamed on AI was caused by AI.
Companies over-hire in the good times. They chase markets that don’t exist. They buy companies and discover they paid twice for the same product. They build things nobody asked for. They miss numbers. They have to answer to investors. Their cost base balloons.
And then AI arrives. And suddenly there’s this beautiful narrative available: we’re transforming the company for the AI era.
Doesn’t that sound better than we made a few strategic mistakes and now we need to cut costs?
I’m not saying every company that blames AI is lying. Plenty of layoffs genuinely are automation-driven. But when a company fires thousands while announcing a billion-dollar AI spend, you should ask how much is really the technology — and how much is restructuring wearing an AI costume. You don’t have to be cynical to wonder. You just have to look at the numbers.
The layoff that never makes headlines
There’s a quieter kind of job loss that nobody talks about.
It’s not the person getting fired. It’s the person who never gets hired in the first place.
A company doesn’t have to fire a hundred juniors. It can just hire thirty fewer people this year. Then thirty fewer next year. Then it stops backfilling roles when people leave. No big announcement, no viral LinkedIn post, no news story. But the opportunities quietly disappear.
This one hits graduates hardest. Entry-level work has always been how people got their start — you do the boring stuff, you watch, you learn, you earn your stripes. If AI eats all the boring stuff, companies ask why they’d hire someone with two years of experience when the tool does most of that work anyway.
Which leads to a question I genuinely can’t answer: how does anyone get five years of experience if nobody will give them the first job?
Management isn’t safe either
And here’s a twist a lot of people don’t see coming: AI doesn’t just threaten the workers. It threatens the managers.
If employees can get answers, build analyses, automate their own workflows and solve problems on their own, then a chunk of traditional middle management stops making sense. A 2026 study published through the Academy of Management found that firms more exposed to generative AI cut managerial hiring by somewhere between 19 and 27 percent, and the evidence points to organizations leaning on middle-management layers a lot less.
Part of me thinks that’s overdue. Some companies have so many layers that a decision has to travel employee → team lead → manager → senior manager → director → VP → executive before coming back, by which point the market has moved. Flatter is usually better.
But there’s a danger too. Slicing out management without rethinking how the organization actually works is chaos. Good management was never just passing information up and down. It’s context. It’s prioritization. It’s accountability, mentoring, conflict resolution, keeping people pointed in the same direction. Cut the dead weight, fine. Cut the leadership, and you’ll feel it.
The cost that doesn’t show up on a spreadsheet
There’s a consequence of all this that never makes it into the quarterly report. Fear.
When people believe the tool next to them is gunning for their job, they change. They stop taking risks. They stop questioning the boss. They stop experimenting. Instead of asking how AI can help us build something better, they start asking how to prove AI can’t replace them. That’s a terrible environment, and companies create it without meaning to.
Innovation needs psychological safety. Fear produces compliance. You can accidentally build the exact opposite of the company you wanted.
The productivity trap
There’s one more quiet way this goes wrong.
AI makes everyone faster. Great. But what if the company doesn’t use that speed to lighten anyone’s load, and just raises expectations instead?
Before AI: “finish this in three days.” After AI: “why can’t you do ten of these today?”
The job doesn’t disappear. But it gets worse. The person is expected to operate like the machine. That’s a different kind of displacement — the role stays, and the human experience of it degrades.
Who actually keeps the gains?
And then there’s the money question.
A tiny company with good AI infrastructure can now take on a much bigger one. One sharp engineer with the right tools can do work that used to need a team. On paper that’s great.
But who captures the value? Does the employee earn more? Does the customer pay less? Does the company invest in new things? Or does the shareholder just get fatter margins?
There’s no law of physics that decides this. It’s a management decision. It’s an economic decision. And right now, most companies seem to be defaulting to the shareholder option.
Forget “will AI take our jobs?”
That’s the wrong question, and I think we all know it by now. The useful questions are smaller and more uncomfortable.
Which tasks disappear? Which jobs change? Which jobs appear? Who gets the productivity gains? Who pays for the transition? And what happens to the people who can’t retrain fast enough?
The World Economic Forum’s 2025 outlook said it well: plenty of jobs created and plenty destroyed by 2030, not some simple collapse. The catch is that job creation and job destruction don’t hit the same people at the same time. A tester who gets let go today can’t become an AI systems architect tomorrow. A junior who never gets hired doesn’t benefit from the senior AI roles five years down the road. A support worker can’t just become a machine-learning engineer because the company tweeted a strategy.
Transitions happen at the population level. People live through them one by one.
The biggest mistake is the mindset
The real mistake isn’t adopting AI. It’s adopting AI with a layoff-first mentality.
The sane sequence is: understand the work → redesign the process → introduce AI → measure productivity → retrain people → redeploy talent → then restructure where you truly must.
What a lot of companies actually do is: buy AI → announce transformation → cut headcount → declare victory.
Those aren’t the same strategy. One is transformation. The other is cost cutting with an AI sticker on the box.
What good looks like
If I could put a sane AI transition in front of executives, it would come down to five questions.
First, what work are we actually automating? Not “which people can we remove,” but “which tasks can the technology do better.”
Second, what happens to the people doing that work? Can they move into harder problems? Become operators of the AI? Work closer to customers? Build new products?
Third, are we measuring productivity or just headcount? Cutting 20% of the team doesn’t mean output went up 20%. It means you have fewer employees. Those are different sentences.
Fourth, are we quietly killing the talent pipeline? Eliminate the junior roles today, and in five years you’ll discover you have nobody left to promote.
Fifth, who gets the dividend? If output really does jump, it shouldn’t just mean more work for the same people. The gains could come back as pay, shorter hours, better products, training, new opportunities. That’s a choice, not a default.
Where this actually lands
I don’t think the future is “humans vs AI.” I think it’s “humans with AI vs humans without AI.” That’s a much more interesting conversation.
The edge probably won’t go to the company with the most AI. It’ll go to the company that best combines people, tools, processes, domain knowledge and management that doesn’t panic. AI is a tool. Management decides how the tool gets used.
So yes — let’s keep talking about AI-driven displacement. But let’s also ask harder questions about corporate decision-making.
When a company cuts thousands while spending billions on AI, ask how much is genuine displacement and how much is strategy wearing a technology costume.
When a company kills its entry-level roles, ask who grows the next generation of experienced people.
When a company celebrates productivity, ask who actually receives it.
And when an executive says “AI is changing the workforce,” ask whether AI is changing the workforce — or whether the executive is using AI to justify changes they wanted all along.
Sometimes the answer is both. That’s okay. The point is to actually ask.
AI isn’t good or bad by itself. And it certainly isn’t responsible for every job lost in this wave. It’s accelerating a bigger transformation — automation, globalization, cost pressure, investor demands, org redesign, new business models. Some jobs vanish, some get better, some new ones show up, and some companies will use this brilliantly while others use it as the handiest excuse they’ve ever had.
The biggest risk was never that AI replaces humans. The biggest risk is that companies replace thoughtful management with AI-shaped cost cutting. Technology can make a bad organization faster at being bad. It can also make a good organization extraordinary.
The difference isn’t the AI. It’s leadership.