Are AI Layoffs Real? Analyzing the Narrative vs. The Numbers

I have noticed a strange pattern emerging on earnings calls lately. CFOs and CEOs keep repeating the same story: AI is driving efficiency, and that efficiency is the reason for recent layoffs.

It makes for a clean narrative. It tells investors that the company is on the cutting edge and cutting costs simultaneously. But when I dug into a recent report from Oxford Economics, the data didn’t back up the story.

In this post, I want to walk through the disconnect between what companies are saying and what the financial numbers actually show.

The Narrative vs. The Data

If you listen to the press releases, you would think algorithms are replacing workers en masse. However, the Oxford Economics report highlights a stark contrast.

In the first 11 months of 2025, they found:

• Total Layoffs: Roughly 300,000 job cuts were tracked.
• AI-Related Cuts: Only about 55,000 were attributed to AI.
• The Reality: That is roughly 4.5% of total layoffs.

The vast majority of job cuts—over 95%—are due to traditional market conditions. We are seeing corrections from the post-pandemic hiring sprees (where companies like Meta and Google nearly doubled in size) and headwinds from tariffs and policy changes.

So why the AI narrative? It seems companies are “re-labeling” routine reductions. Telling the market you are firing people because of “AI productivity gains” sounds much better for stock prices than admitting to poor demand or over-hiring.

The Inflationary Reality of AI

There is a common belief that technology is deflationary—it makes things cheaper and faster. While that might be true eventually, right now we are seeing the opposite.

We are in a massive build-up phase. The competition for scarce resources is driving costs up, not down.

• Chips: Gaming computer prices are skyrocketing (source) because manufacturers are shifting capacity to enterprise AI chips.
• Energy: Data centers consume massive amounts of power, raising rates in local markets.
• Infrastructure: The CapEx spending on data centers is in the billions.

This spending is creating inflationary pressure. Markets are now expecting inflation to run above the Fed’s 2% target through 2027 (Reuters), largely driven by these input costs.

Accounting Implications: The Depreciation Trap

For those of us in finance, this infrastructure spend creates a specific headache regarding depreciation and useful life.

Companies often assign a 5-to-7-year useful life to hardware assets. In a normal environment, that works. But in the current AI arms race, NVIDIA or AMD might release a chip next year that is exponentially faster than what you bought today.

If a chip becomes obsolete in 18 months, but you are depreciating it over 7 years, your books are not reflecting reality. This brings up serious impairment risks. I’ve looked at this previously regarding Michael Burry’s positions—if the hardware loses value faster than the accounting schedules allow, corporate balance sheets might be overstated.

Tinfoil Hat Corner

Here is a speculative thought: The stock market is sitting at record highs in 2025, and almost all that growth is driven by AI bets.

The market is pricing AI success as a certainty. They are assuming the productivity boom is guaranteed. But if we hit 2026 and companies cannot prove a tangible P&L impact from all this spending, we could see a harsh correction. If the “AI Narrative” cracks, the valuations built on top of it might tumble.

Key Takeaways

• Don’t Trust the Hype: Only a small minority (approx. 4.5%) of layoffs are actually caused by AI. The rest are standard economic cycles.
• Inflation is Sticky: The rush to build data centers and buy chips is keeping input costs high, likely keeping inflation above 2% for years.
• Watch the Depreciation: Standard accounting schedules may not move fast enough for AI hardware obsolescence.
• Stock Valuations are Fragile: Much of the current market value is based on future promises of AI productivity that haven’t fully materialized in the P&L yet.

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