The article argues that recent layoffs at profitable firms are being misread as AI-driven job replacement. The real drivers are post-pandemic demand normalization after the 2020–2022 hiring boom and the repricing of capital once rates jumped, which made boards and investors demand visible efficiency. Layoffs became a signal of discipline and margin protection, often paired with AI and data-center commitments.

AI matters mostly as framing and capital-allocation justification. Productivity gains are hard to measure, but headcount cuts show up immediately in revenue-per-employee, so executives cite AI to explain why labor costs must fall now. The cuts also reshuffle power by trimming recruiters, coordinators and middle managers while protecting core engineers and AI specialists, producing leaner, centralized firms. The article concludes this is rebalancing, not collapse, and urges leaders to base decisions on regime shifts and measurable signals, not headlines.

Most large companies have formal policies, enterprise licenses, internal copilots, or approved tool stacks. In many sectors, AI is already embedded in day-to-day work. If adoption alone were the constraint, we should already see it in the productivity data.

And yet, the aggregate numbers remain underwhelming.

This tension is often framed as disappointment or hype fatigue. I think it is better understood as a timing and measurement problem. In this post, we will suggest a different, slightly more uncomfortable explanation to the fears of an AI bubble.