The Next Round of Layoffs Is Coming, and It’ll Be Brutal

A family friend works as a recruiter for a large consulting firm. I'll call her Rachael. One week, she told me there were fewer interview slots. The next, offers sat unsigned. Then a few searches were paused without explanation. Same titles. Same headcount plans. Just delayed. She kept her pipeline warm anyway because that's what she's paid to do.

Then the job descriptions started changing. Apparently, each one had fewer responsibilities listed and a broader scope. Phrases like "supports automation initiatives" added near the top. She was also told to screen for candidates who were "comfortable working alongside AI tools."

Rachael said something else changed too.

Hiring managers stopped asking how long a role would take to ramp. Instead, they asked how soon output could be measured. They wanted proof fast. Metrics early. Anything that looked slow was flagged and anything that could be templated fast-tracked as quickly as possible.

Last month, Rachael was asked to close three open roles. The work didn't disappear. It was just reassigned to a mix of ChatGPT wrappers and a skeleton crew of staff. So, she updated her spreadsheet and sent the standard emails. Searches closed. Candidates thanked. Positions "re-evaluated."

Most people think job losses follow bad quarters or missed forecasts. But this time, things are different. AI tools are being deployed to absorb work, measure tolerance, and prove that fewer people are needed. And once leadership sees the business still runs, the cuts aren't a reaction. They're the final line in a process that already assumed people were expendable.

I know a lot of people who work in tech. They're the type to plan ahead, keep their skills current, and never miss deadlines. None of them thought they were exposed to the possibility of a layoff as leadership kept talking about growth, efficiency, and long term bets.

One friend worked on a product team at a cloud software company. His role was stable and he'd been there for several years. Then one quarter, his manager asked everyone to write down what they owned and how often it shipped. The next quarter, a new tool was introduced to speed things up. And sure enough, his job disappeared a few months later.

Another friend worked in data operations. His job was preparing inputs before models touched them. But then a system was introduced to automate part of that work. He trained it, corrected errors, and logged failures. But once it started producing usable results, his workload dropped. And eventually, he was told his position had been made redundant as the company had learned to operate more efficiently.

"They needed proof the company could run with fewer employees thanks to AI," he told me. "Once they had it, dozens of us had to find new jobs."

What's happening now isn't just about removing jobs. It's about removing responsibility as decisions that used to belong to executives are being outsourced to dashboards and vendor claims. "The model recommends it." "The system supports it." AI becomes the excuse that absorbs the backlash. And the same people who trained the tools, fixed the errors, and kept things running are the first to be removed. Not because they had a poor work ethic. Rather, because they succeeded at making themselves replaceable.

The same claims show up all the damn time in earnings calls, investor decks, and policy briefings. "AI will create more jobs than it removes. Efficiency gains will unlock new roles. The labor market will adjust." The assumption is that output growth naturally leads to employment growth.

That assumption doesn't match how hiring works.

AI is deployed to reduce labor cost. Roles are combined. Tasks are absorbed. Headcount is trimmed while targets remain fixed. Companies track output per employee, cost per unit, and time to delivery.

New roles do appear, but they're limited and narrow. They require prior technical exposure, longer hours, and higher tolerance for instability. Pay increases pool at the top. Everywhere else compresses. Mid level roles disappear. Entry level slots stop opening. Internal movement stalls. And sadly, the career ladder people used to climb no longer exists.

We're telling people AI will strengthen the job market at the same time companies are using it to remove jobs. Executives say the tools raise productivity. That efficiency creates opportunity. That losses in one place will be offset somewhere else. Those claims aren't abstract. They're written into workforce plans, cost models, and internal targets that all point to the same outcome of fewer people doing the same work.

At the same time, the largest employers are already acting on that logic. Amazon recently cut tens of thousands of roles while expanding automation across warehouses, logistics, and internal teams.

"AI is the most transformative technology we've seen since the internet," said Beth Galetti, senior vice president of people experience and technology at Amazon. "The company needs fewer layers to move as quickly as possible."

More companies are getting in on the action. UPS cut thousands of jobs while the well-paid execs froth at the mouth as to how AI will improve its business. "Technology has changed so much in the past year when you think about the advent of generative AI and applications inside our business," said Chief executive Carol Tomé. "I'm really excited about what the changes will mean."

Employment is supposed to provide stability. Markets are demanding its reduction, lower headcount, faster output, and higher margins. Any company that keeps extra staff looks inefficient next to one that doesn't. Any pause looks like waste. Cutting roles is treated as execution, not harm. After all, Dario Amodei, CEO of Anthropic, a leading AI company, said that AI could wipe out half of all entry-level white-collar jobs in five years and increase unemployment in the US to 20%.

At some point, this stops being a labor issue and becomes a governance one. Large employers reduce payroll. Tax bases shrink. Public services lose funding. Safety nets get strained by demand they weren't designed to handle. People don't blame systems or models. They blame institutions and the people in power that promised work would always be available.

After the next round of mass layoffs, the damage stops being evenly distributed. Regions hollow out. Younger workers leave. Older workers get stuck. Housing markets freeze in some places… and collapse in others. Stability has to be enforced rather than earned and eligibility criteria for welfare programs tighten because the government can't afford to give Universal Basic Income to hundreds of millions of people. And at that stage, the problem isn't unemployment. It's governing a population that no longer believes the system is built to include them.

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