Even as Wall Street banks post record earnings, AI-driven layoffs continue, raising concerns about cascading macroeconomic shocks

Wall Street’s strategy of maximizing efficiency
Expansion of white-collar layoffs amid AI adoption
Potential to trigger consumption contraction and financial instability

Major Wall Street banks are moving ahead with workforce reductions even as they report record earnings. Despite surging profits fueled by expanded trading activity and regulatory easing, financial institutions are accelerating cost-efficiency initiatives through the adoption of artificial intelligence (AI), launching a broad restructuring of their employment structures. Experts warn that these changes could evolve into a new macroeconomic risk capable of undermining both consumption and financial market stability.

Restructuring Across Core Divisions Despite Record Profits at Wall Street’s Six Largest Banks

On the 5th (local time), The Wall Street Journal (WSJ), citing multiple sources, reported that Morgan Stanley is implementing staff reductions across key business units including investment banking (IB), trading, wealth management, and asset management. The layoffs are understood to be a company-wide adjustment extending beyond the U.S. headquarters to overseas offices. Major reduction procedures began on the 4th, with some workforce cuts already carried out in phases since the previous week. The restructuring includes numerous employees in support organizations such as back-office staff, as well as private bankers (PBs) in the wealth management division and personnel responsible for mortgages for high-net-worth clients. The move reportedly reflects a combination of strategic business realignment, regional priority adjustments, and performance evaluation outcomes.

The restructuring has drawn attention precisely because it is not driven by weak performance. Morgan Stanley, which employs approximately 83,000 staff, recorded annual revenue highs in both its IB and trading divisions and its wealth management division last year. The wealth management unit, which accounts for roughly half of the firm’s total revenue, posted particularly strong growth, with fourth-quarter revenue rising 13% year-on-year. On the surface, the company appears to have little reason to reduce headcount based on performance alone, yet the firm is said to have justified the move as part of an effort to streamline its cost structure and reinforce long-term competitiveness.

This wave of workforce reduction is spreading across Wall Street. According to Bloomberg, the combined headcount of the six largest U.S. banks—JPMorgan Chase, Bank of America (BOA), Morgan Stanley, Wells Fargo, Citigroup, and Goldman Sachs—stood at 1.09 million as of the end of December last year, the lowest level since 2021. The total represents a decline of roughly 10,600 employees from the previous year and the largest annual reduction since 2016. Wells Fargo recorded the largest cuts, reducing more than 12,000 jobs last year alone, with the possibility of further restructuring still being discussed. Citigroup has also reduced its workforce by more than 3,000 employees and has plans to eliminate an additional 1,000 positions in the future.

All of these banks delivered massive profits last year amid favorable conditions, including a strong U.S. equity market, expanding corporate deal activity, and deregulation policies under the Donald Trump administration, which boosted trading revenues. The six banks generated combined revenue of $593 billion last year, setting a new record. Net income reached a combined $157 billion, approaching the levels recorded in 2021 when accounting effects temporarily inflated profits. During the same period, the six banks returned more than $140 billion to shareholders through dividends and share buybacks, marking the largest capital return on record.

The business outlook for this year remains broadly optimistic. Jeremy Barnum, chief financial officer (CFO) of JPMorgan, noted that “the constructive momentum we are seeing is reflected in our pipeline (Pipeline, potential deal list).” Bank of America CFO Alastair Borthwick also said that “solid growth momentum is evident in IB fee revenues.” Goldman Sachs chief executive officer (CEO) David Solomon stated in an interview with the Financial Times (FT) that the firm has secured its largest deal backlog (Deal Backlog, contracted deal pipeline) since the COVID-19 pandemic as more companies seek to capitalize on the U.S. deregulatory environment.

Cost Efficiency from AI Adoption Accelerates Structural Shifts in Financial Industry Employment

The reason for workforce reductions despite strong earnings is clear. Staffing levels expanded during the pandemic to accommodate surging trading volumes and asset management demand have now become excessive under current operating conditions, while AI-based automation systems are increasingly replacing those roles. The key point is that transaction volumes themselves have not declined; rather, the methods used to process those transactions are changing rapidly. AI has already been introduced across numerous operational areas in the financial industry—including trading, risk management, wealth management analytics, and client services—and is rapidly replacing some repetitive, data-driven tasks.

The inclusion of private bankers in Morgan Stanley’s restructuring has drawn particular attention. Traditionally, PB roles have been regarded as a quintessential form of relationship banking built on trust with high-net-worth clients. The fact that positions rooted in face-to-face trust building have been included in layoffs has prompted widespread commentary on Wall Street that “no area is immune from restructuring.” Analysts interpret this as a signal that AI agent models are beginning to influence not only back-office functions but also front-office activities such as wealth management and investment advisory services.

Global consulting firm McKinsey & Company recently estimated in a generative AI report that AI agent technologies could generate an additional $200 billion to $340 billion in annual value for the banking industry. The report argues that productivity gains in high-skill tasks such as data analysis, research, and client management could enable financial institutions to transition toward organizational structures that maintain high efficiency with fewer employees. The International Monetary Fund (IMF) has likewise projected that roughly 60% of jobs in advanced economies could be affected by AI. Unlike earlier waves of automation, AI is seen as having significant potential to transform the work patterns of high-income professional occupations.

Mass Layoffs → Consumption Collapse → Financial Instability Spiral

However, the macroeconomic implications of widespread white-collar job reductions could be substantial. Citrini Research, a firm closely followed by Wall Street hedge funds and institutional investors for its macro-risk analysis, argued in its report “The 2028 Global Intelligence Crisis,” published last month, that while AI can enhance corporate productivity, it could simultaneously weaken employment and wage foundations, potentially triggering a sharp contraction in consumer spending. The report suggests that such a consumption downturn could spread to private credit, insurance, and mortgage markets, transmitting systemic shocks throughout the broader financial system.

The report presents its scenario in the form of a hypothetical macroeconomic analysis dated June 2028, two years in the future. In the scenario, as AI agents replace coding tasks, workflow automation, brokerage functions, and elements of decision-making, companies reduce headcount, while households facing declining incomes scale back consumption. Firms under revenue pressure respond by further increasing AI investment in an effort to defend profitability. Under this scenario, unemployment rises to 10.2% and the Standard & Poor’s (S&P) 500 index falls 38% from its peak. Citrini Research explains that the scenario does not represent a simple forecast but rather models a “Left-tail Risk,” a low-probability yet high-impact outcome that markets have not sufficiently examined.

Ironically, the early phase of AI expansion could appear similar to an economic boom, the report argues. Corporate margins improve as companies reduce headcount, and rising profits are reinvested into AI computing infrastructure. Productivity and nominal gross domestic product (GDP) may also show solid growth, while equity markets could rally on expectations of expanded AI infrastructure investment. However, the report describes this phenomenon as “Ghost GDP.” The concept refers to a situation in which production increases but the resulting income does not flow back to households, preventing consumption from expanding. In this dynamic, the contraction of white-collar employment emerges as a critical variable, as a significant portion of U.S. consumption is sustained by higher-income households.

If high-income professionals lose their jobs or shift into lower-paying occupations, consumer spending could decline more sharply than the absolute reduction in employment would suggest. If these workers move into service industries or platform-based gig work, downward pressure would also be exerted on wages in those sectors. The result could extend beyond a typical cyclical slowdown, evolving into a shock that destabilizes the upper tier of income distribution. Citrini Research identifies enterprise software—particularly software-as-a-service (SaaS)—as a key trigger for this dynamic. As AI-driven coding tools advance, companies may attempt to replace expensive subscription-based software with internally developed systems. SaaS models that rely on “per-user pricing” face a structural vulnerability: when corporate clients reduce headcount, license revenues shrink simultaneously. Layoffs accelerate AI adoption, and AI adoption in turn fuels further layoffs, reinforcing a self-perpetuating cycle.