AI Didn't Take Your Job—Bad Business Did

Some things are better left observed.

Any day of the week, I get a news email or feed stating that AI is coming for all these jobs. What's actually happening: the majority of layoffs labeled "AI-related" are a convenient excuse for companies to avoid admitting their business isn't doing well, and the other half will find that while AI might work for a narrow subset of tasks, LLMs and their derivatives still can't fully replace most positions in 2025.

But fear sells. Tell everyone AI will take their job, and you've got a great news bite or YouTube view count.

I remember 2008 like it was yesterday—watching mass layoffs sweep across the country. Setting aside the economic complexity of mortgage-backed securities, there was nowhere to hide once the domino effect began.

Then, as now, if a company has a chance to save face about why it's conducting layoffs, it will take whatever route keeps it from admitting it didn't play the game correctly, mismanaged the business, or was just plain reckless.

Today, if your business isn't doing well but you can frame layoffs in a way that doesn't set the ship on fire, you're going to take that lifeline. And right now, there's no better lifeline than blaming AI. It beats having to explain that the broader market is down and being propped up by AI companies and their chip suppliers.

What do the numbers actually show?

Challenger, Gray & Christmas—one of the main and most widely cited firms tracking U.S. layoff announcements—estimates that about 12,700 jobs in 2024 and roughly 55,000 in 2025 were explicitly attributed to AI. "Attributed" is key here, because organizations aren't required to prove AI is actually replacing these jobs.

The team collects data from company announcements, press releases, earnings calls, and regulatory filings, then codes the "reason" based on the language employers use. Their 2025 year-end report notes that "AI was cited for 54,836 announced layoff plans"—meaning they're counting cuts where AI appears as the stated reason, not cases where they've audited internal workflows to prove AI directly replaced those workers.

Even in 2025, when AI-cited cuts jumped, those 55,000 jobs were only about 4–5% of total reported layoffs.

Why not say the layoffs are AI-related? It gives companies a chance to save face by framing them as "AI efficiencies," which lends the story a more strategic, forward-thinking tone.

Now, if you're wondering about all this hoopla about AI taking over full jobs—the answer is no, it's not.

Generative AI could eventually raise workforce productivity by 15%, according to Goldman Sachs. Sounds dramatic, right? Except they also note that AI might displace 6-7% of the US workforce if widely adopted, but "the impact is likely to be transitory as new job opportunities created by the technology ultimately put people to work in other capacities."

Read that again: productivity goes up, some jobs shift around, people land elsewhere. The sky isn't falling—work is just changing shape.

The reality of LLMs and generative AI: they can greatly increase the speed of certain business tasks—preliminary research, some types of coding, and various other functions. They can significantly accelerate a business’s operations. But we're still far from generative AI completely replacing humans for tasks that require judgment, deep thinking, creativity, or meaningful human interaction.

Generative AI tools like today's chatbots don't have human-style understanding or goals. They're trained to predict the next likely word based on patterns in massive text datasets. They can dramatically speed up many business tasks, but they still rely on humans to define objectives, verify outputs, and take responsibility for real-world decisions.

So the next time you read, hear, or see the latest "expert" telling you that AI is taking over your jobs and somehow explains all the layoffs, remember this: it's far more likely that the economy is struggling, businesses are bleeding out, and layoffs are inevitable. But we'd all rather be told it's about technological advancement than face the reality of poor management and a stagnant economy.