Microsoft (MSFT.US) provides voluntary buyout programs to about 60,000 people: structural divergence in US employment under AI restructuring

According to Woofun AI, the US labor market in the fall of 2026 is experiencing a profound structural divergence: while the number of first-time jobless claims remained at a 57-year low of nearly 194,000, and the overall unemployment rate was only 4.2%, only 29,000 new jobs were added in non-farm payrolls in September, and recruitment and turnover rates fell into a slump at the same time.

This “low layoff, weak recruitment” phenomenon is not simply a cyclical fluctuation, but rather a profound transformation triggered by AI-driven efficiency restructuring, marked by Microsoft (MSFT.US) providing a voluntary buyout plan for about 60,000 employees and American Express (AXP.US) announcing a reduction of about 5% (about 3,500 people) of the global workforce.

The calm of macro data is in stark contrast to the aggressive micro behavior of enterprises. The number of people who continue to claim unemployment benefits is around 1.7 million, indicating that large-scale mandatory layoffs are still in the minority. Companies prefer to adjust the structure through natural attrition, such as encouraging early retirement, accepting voluntary separations, and not filling vacancies. Microsoft's voluntary buyout program covers the eligibility scope of about a quarter of global employees and is essentially a voluntary withdrawal; while Express directly emphasizes preparing for AI deployment to improve organizational agility and decision-making efficiency.

Data compiled by Woofun AI shows that this model of promoting AI efficiency restructuring through voluntary buyouts and staff reductions is reshaping the human cost structure of tech giants, making workers relatively safe, and job seekers facing an extremely difficult market environment.

The fragmentation of the job market has further exacerbated the reversal between industries. The re-employment cycle was extended, and the 20-24 age group, fresh graduates, and inexperienced entry-level job seekers were the hardest hit, and the unemployment rate was significantly higher than the overall level. The reason is that companies are conservative in their recruitment intentions, prioritize retaining experienced employees, and use AI tools to let a few highly skilled people take on tasks originally performed by multiple junior employees, thereby reducing entry positions.

At the same time, there has been a partial reversal in the historical pattern of recession caused by the traditional contraction of the construction and manufacturing industries, which are sensitive to interest rates. Employment in non-residential buildings has expanded significantly due to data center construction, driving a recovery in the consumer durable goods manufacturing industry (such as cooling equipment, power equipment);

Despite a moderate contraction in residential construction, overall construction and manufacturing employment has shown a net increase, which has become a key factor supporting recent economic resilience, while service and white-collar jobs are facing a different logic of pressure.

Instead of translating into widespread real income growth, productivity increases have exacerbated distribution imbalances. Information technology, finance, and professional services are the industries with the highest rate of AI adoption, and the Census Bureau survey showed a clear upward inflection point in actual output/worker metrics in these areas in late 2022 to early 2023. Together, these high-adoption industries account for about 15% of the labor force. Preliminary estimates show that about 80% to 85% of the productivity gains in AI-related industries currently flow to corporate profits, and only a small amount is converted into actual wages. As a result, nominal wages increased by about 3.0% per year, while the consumer price index was about 3.7%, leading to a slight negative increase in real wages. Workers who have long been accustomed to a real wage growth rate of about 2% to 2.5% have been low in the face of increased tolerance for large expenses such as housing and automobiles, and the Michigan Consumer Confidence Index has been low for a long time.

In this self-strengthening cycle, companies reinvest additional profits into AI infrastructure, while small businesses (accounting for more than 40% of employment and GDP) face higher uncertainty. The NFIB survey showed that in September 2026, their optimism index was slightly below the long-term average, the uncertainty index was significantly higher than the historical average, and labor quality, inflation, tariff policy fluctuations, and energy price shocks made recruitment plans cautious.

In the future outlook, the Federal Reserve's policy remains a key variable. The current leading indicators, with the exception of residential construction, have improved overall, and corporate profits have remained resilient, making the economy able to absorb interest rate hikes to a certain extent. However, if policies continue to be tightened and lead to a marked weakening of employment in construction and manufacturing, cyclical pressures may compound and amplify existing structural frictions. The continued moderate decline in residential construction employment requires close attention, as it has historically led the recession. Real wage growth is unlikely to improve substantially in 2027. Deep structural factors include negative externalities caused by excessive government debt and spending, which inhibit real income growth in the private sector. Currently, the market is in transition. Blue-collar workers are supported by data center construction, and entry-level white-collar jobs are under pressure. Policy responses should focus on lowering the threshold for retraining, improving entry job creation mechanisms, and preventing the expansion of government from putting long-term pressure on the actual revenue of the private sector. The definition of “safety” in the labor market is being redefined, from simply being unemployed to being able to continuously gain cumulative experience and real income growth in a rapidly changing technological environment.