Why Young Professionals Are Running Out of Rungs on the Career Ladder
By The Craig Bushon Show Media Team
AI isn’t coming for jobs. It’s already here—and it’s already replacing them.
For years, Americans were reassured that artificial intelligence would be a tool, not a threat. We were told it would “augment” workers, not eliminate them. We were promised a future where productivity gains lifted everyone.
That promise is collapsing in real time.
In Charlotte, North Carolina—the second-largest banking center in the United States—two of the city’s largest employers, Bank of America and Wells Fargo, have begun openly acknowledging what workers have already felt: AI is reducing the need for human labor.
Executives rarely say “layoffs.” They talk about “efficiency,” “automation,” and “doing more with less.” But when technology replaces tasks that once required people, the outcome is the same—fewer hires, slower advancement, and shrinking opportunity.
At Bank of America, AI-assisted coding tools have eliminated work once done by thousands of developers. At Wells Fargo, workforce reductions continue as automation expands deeper into operations.
This is not a future scenario. It is policy in motion.
Banking was the first wave.
Tech followed quickly.
Law entered the compression phase.
Accounting is now feeling the squeeze.
And in early 2026, consulting has moved squarely into the crosshairs.
Across strategy consulting, professional services, and advisory work, the same economic logic applies. Consulting has always depended on a large base of junior analysts and associates—young professionals doing research sweeps, data gathering, modeling, slide building, and early synthesis that feeds senior judgment. Much of that work is repetitive, pattern-based, and format-driven.
That is exactly the kind of cognitive labor AI now automates at scale.
Internal platforms at firms like McKinsey & Company, Boston Consulting Group, and Bain & Company, alongside the Big Four—Deloitte, PwC, EY, and KPMG—now handle research synthesis, data analysis, draft reports, scenario modeling, and even early client presentations. Firms report 30 to 50 percent time savings on routine tasks, allowing partners to oversee more projects with smaller teams.
The immediate impact shows up where it always does: at the bottom of the pyramid.
Graduate hiring slows. Start dates are delayed. Junior cohorts shrink. The work still gets done—but with fewer people.
This same pattern is playing out in law.
Courtroom advocacy, ethical judgment, strategy, and client relationships remain deeply human. But support and routine cognitive roles are being compressed rapidly. Paralegals, legal assistants, and researchers face some of the highest automation exposure in the white-collar economy. AI systems now handle document review, e-discovery, contract analysis, and first-pass research with accuracy that is often “good enough” for internal use.
Junior lawyers are not immune. Entry-level drafting, precedent matching, and initial analysis—the traditional training ground—are increasingly automated. Firms produce more output with fewer associates. Billable hours persist, but margins rise as headcount flattens.
Accounting followed the same path.
Bookkeepers, payroll clerks, junior staff accountants, and roles dominated by reconciliations, invoice processing, expense reporting, and transaction matching are being automated by modern platforms and agentic systems. AI closes books faster, flags anomalies instantly, and allows firms to handle more clients with fewer entry-level staff.
Official labor projections still show openings due to retirements and turnover. What they obscure is distribution. Growth concentrates at the top—experienced, strategic, advisory-heavy roles—while entry-level pathways narrow.
And now, consulting.
Firms advising clients on AI disruption are living it internally. Junior analyst work that once justified large intake classes is increasingly automated. Hiring pipelines compress quietly. The leverage model changes. AI literacy becomes mandatory. Soft skills—judgment, storytelling, stakeholder management—matter more than raw hours worked.
This is the great white-collar layoff in its modern form.
It does not arrive as one dramatic purge.
It arrives as slower hiring.
As attrition that is never replaced.
As fewer entry points into six-figure careers.
As ladders that quietly get shorter.
And that leads to a problem few institutions want to confront.
The education and credentialing system has not adjusted.
Students are still encouraged to take on massive debt—MBAs, JDs, CPAs, specialized master’s degrees—based on career ladders that are being compressed after the debt is incurred. Universities, credentialing bodies, and employers continue selling a promise of upward mobility while AI quietly removes rungs from the bottom.
This creates a dangerous feedback loop: high-cost education, fewer entry-level roles, slower advancement, and rising disillusionment among young professionals who did everything they were told to do.
Follow the money, and the incentives become clear.
AI productivity gains do not disappear. They flow upward—to equity partners, senior executives, shareholders, and private equity owners. Output rises. Headcount flattens. Margins expand. The value created by automation is captured at the top while opportunity narrows below.
This is not malice. It is economics.
There is also a regulatory and liability blind spot emerging.
Firms still bear full responsibility for errors—legal, financial, strategic—even as fewer humans remain in the loop to catch AI mistakes. AI works until it doesn’t, and the systems that generate speed also reduce redundancy and review. This mismatch between automation and accountability is a future scandal waiting to happen.
The regional impact compounds the problem.
White-collar hubs like Charlotte and Nashville depend on junior churn—apartments, restaurants, childcare, car sales, and small businesses rely on early-career professionals entering the workforce. Slower hiring is the silent killer of regional economies. When the bottom slows, the ripple effects move outward fast.
And beneath it all is a cultural cost we rarely measure.
Trust.
Workers were told: get educated, work hard, climb the ladder. AI compresses the ladder after buy-in. The damage is not just economic—it is psychological. Promises are broken quietly. Cynicism replaces loyalty. Institutions lose credibility.
Yes, AI creates opportunity. New roles emerge in governance, oversight, risk, and strategy. Strong professionals who learn to leverage AI can thrive. Mid-level workers who combine experience with tools often benefit most.
But average performers—and especially entry-level workers—feel the squeeze first.
The uncomfortable truth is this: AI does not need to replace everyone to destabilize everything. It only needs to compress the ladder enough that fewer people can climb it.
Across banking, tech, law, accounting, and consulting, the pattern is now undeniable. The great white-collar layoff is incremental, deniable, and economically rational—until the cumulative damage becomes impossible to ignore.
The technology has already moved on.
The question is whether leadership, education, and policy will catch up before the middle class absorbs the full cost of “efficiency.”
Bottom line: this is not a collapse. It is a hollowing. And hollow systems eventually fail from the inside.
This is The Craig Bushon Show—where we don’t just follow the headlines, we read between the lines to get to the bottom line of what’s really going on.
Disclaimer:
This op-ed is commentary and analysis intended for informational purposes only. It reflects publicly available research, industry reporting, and economic trends and should not be construed as legal, financial, or employment advice.
