The AI Layoff Lie
Companies are cutting jobs and calling it productivity. The numbers say otherwise.
Hey All, real John here. I had a great lunch this week, and it wasn’t the food, but the company. I sat at a table with head of QA and a few amazing engineers. What did we talk about? AI of course. How is AI doing, did you hear about <blah,blah,blah> laid off 100 workers or more. Well, they claim one thing, because it is ok to say it: AI has improved our process so we have reduced our workforce by 30%. Let’s be honest, we over-hired, had some mediocre or poor employees and don’t want to get sued for wrongful termination, so we claim AI and perform a RIF. What happens next? The good employees are now worried about their jobs, take on the additional responsibilities for survival, and the company posts record revenue with “AI”, because they laid off 30% of their payroll, their most expensive cost, and often their most valuable asset. Anyway, that’s my initial rant on that, but I handled the rest of this newsletter a lot more professionally… but before I do, When people are moved to words like “resources” to allocate, the dehumanization of the person makes the decision to fire or hire someone is no longer a holistic view that incorporates empathy, but simply becomes an object you move around like a chair… It’s been several years since I worked at a place like that and it makes me love my current role when I think back to that. It is the pain that you endure and survive that defines you more than any accomplishment or success. You are wired to always avoid pain first, not pursue joy. Four of Spades - if you know you know… let’s get into it.
I want to be clear about something upfront: AI is real. The tools are getting better every week. I use them daily, I build with them, and I write about them constantly. I’m not here to do the tired “AI is overrated” take.
But what’s happening in corporate America right now isn’t an AI transformation. It’s a rebranding of cost-cutting — and the productivity gains they’re promising? They aren’t showing up yet.
Here’s what the data actually says — and more importantly, what you should do with it.
The Numbers Don’t Lie (The CEOs Do)
In Q1 2026, tech companies laid off nearly 80,000 workers. About half of those cuts were officially attributed to AI-driven efficiency. Amazon, Meta, Oracle, Salesforce, Block, Atlassian — all announced massive headcount reductions while explicitly crediting AI productivity gains.
Except there’s a problem.
A Duke University CFO survey, conducted with the Federal Reserve Banks of Atlanta and Richmond, found a significant gap between perceived and actual productivity gains from AI. Their words: executives are seeing potential, not financial results. “It’s not really showing up yet in revenue,” said the study’s co-author.
Cognizant’s own Chief AI Officer said the quiet part out loud: “I don’t know if they are directly related to actual productivity gains. Sometimes AI becomes the scapegoat from a financial perspective — like when a company hired too many, or they want to resize, and it gets blamed on AI.”
Even Sam Altman — the guy whose company profits most from enterprise AI adoption — admitted at a summit: “There’s some AI washing where people are blaming AI for layoffs that they would otherwise do.”
So what’s actually happening? Companies over-hired during the zero-interest-rate era. Capital got expensive. Investors want margin expansion. “AI made us efficient” is a much better story for the board deck than “we hired too many people and now we’re fixing it.”
The workers being cut are largely not the workers being hired. There are 275,000 AI-related job postings sitting unfilled right now — with a 56% wage premium. The shuffle is real. The productivity story is, at best, premature.
What This Means If You’re a Builder
If you’re building a product or running a team, here’s how to actually think about this moment — practically.
1. Don’t use “AI efficiency” as a headcount strategy before you’ve measured anything.
If you’re cutting roles because you think AI will cover the gap, you’re betting on potential, not performance. Cognizant’s own AI chief says expect 6–12 months before real gains materialize. Build the measurement framework first. Know what “improved productivity” means in your context before you cut the people who currently hold the thing together.
2. Audit what your team is actually doing with AI tools today.
Not what they say they’re doing. What they’re actually doing. Where are the real time savings? Where is AI creating new work (prompt wrangling, output review, rework)? You won’t know until you look. Most teams have a handful of people getting 10x productivity gains from AI and a majority still using it like a fancy search engine.
3. If you’re going to cut, cut honestly.
“We over-hired and need to right-size” is a harder sentence to say, but it’s cleaner than a narrative that blames AI for a decision that was always going to happen. Your remaining team knows the truth. They watched what got cut. Respect their intelligence.
4. Use this moment as a competitive advantage if you’re small.
Here’s the flip side: large companies cutting experienced people to fund AI initiatives creates an enormous opportunity. Those 275,000 open AI roles? Most of them are at the companies who just laid off people who could do that work with six months of reskilling. If you’re a smaller team, you can move faster. Hire one of those people. Build the thing they couldn’t get approved. The talent market right now is genuinely interesting for scrappy operators.
5. Don’t confuse activity with productivity.
This is the sneaky one. AI makes it very easy to look busy — more drafts, more proposals, more code output. But are you shipping things that matter? Are you solving the right problems faster? I built Cash Critters for $50 a month with AI tools, but the AI didn’t tell me what to build or why. That judgment is still yours. Don’t mistake token generation for value creation.
The Actual Opportunity
The real story here isn’t doom-and-gloom about job losses. It’s that most enterprise AI “transformations” are theater, which means anyone who’s doing it for real has a massive head start.
The companies treating AI as a budget justification rather than a capability investment are creating a gap. They’re cutting the people who understand their workflows and replacing them with... nothing yet. Promises. Slides. Roadmaps.
Meanwhile, the builders — the scrappy ones who are actually integrating AI into how they work — are lapping them.
The productivity gains are coming. They’re just not on the timeline executives are selling to shareholders. And the companies who survive the gap between “we cut for efficiency” and “efficiency actually arrived” are going to be the ones who built real capabilities instead of performing them.
That’s your window. Use it.
Go build something that actually works.
John Mann is the founder of Startups and Code LLC, a software engineering executive, and the guy who built Cash Critters for $50/month because constraints are a feature, not a bug. Subscribe for weekly takes on AI, startups, and building things that matter.