The 2027 layoffs will come, but AI will be more alibi than cause, according to a Seeking Alpha analyst

By Seeking Alpha · October 26, 2026.

Bret Jensen, an analyst and investor who publishes on Seeking Alpha, argues that US layoffs will accelerate in 2027 and that, regardless of what corporate leaders say, AI will not be the main cause. This is the opinion of an individual author, not official data or a study.

According to his description, the 2026 US labor market is in a "no hiring, no firing" environment. Weekly jobless claims are hovering near their lowest levels in generations, and labor force participation remains well below its historical highs.

Jensen argues that job losses attributable to AI are still limited. In his view, many companies cite AI as a margin-boosting justification rather than as a genuine displacement of workers.

The second pillar of his thesis is financial. He notes that hyperscalers such as Oracle and Microsoft face rising capital expenditure, negative free cash flow and skepticism in debt markets, even as they keep building AI infrastructure. He also cites a $95 billion secondary offering by Meta and slower growth in AI infrastructure as potential brakes on the stock market wealth effect and on future job creation. The accessible summary does not elaborate on the figures or sources behind these points, so they should be taken as the author's reading rather than verified facts.

**Our take.** The thesis has a valuable core: "AI is taking our jobs" is a convenient explanation for almost any cut. If a company needs to recover margin because its data center investment is not paying off, attributing the adjustment to AI sounds more modern than admitting a balance-sheet problem. It is a variant of what is generally known as "AI washing": using AI as a narrative alibi.

But it is best not to overcorrect. The fact that measurable displacement is limited today does not mean it will remain so. Both forces can coexist: an investment cycle squeezing the books and genuine automation of coordination and support roles, which is where we are already seeing the first cuts. Separating how much is due to each is difficult, and not even institutions know how to measure it well in aggregate employment.

For the reader, the practical lesson is one of method: when a company announces layoffs "because of AI," look at its cash flow, capex and cost structure before accepting the narrative. In the short term, the transition will be hard and uneven, and worse still if it is compounded by an adverse financial cycle. In the long term, our thesis is unchanged: the technology points toward an abundance that frees people from forced labor. But getting there requires honest diagnoses today, and understanding which cuts are structural and which are simply the economic cycle.