The Crisis of Entry-Level Labor in the Age of AI (2024–2026)

TL;DR

The traditional “deal” of entry-level work—trading rote labor for mentorship—is dead. And if not quite dead yet, it really is dying (unlike all those other things that were supposed to die years ago).

Our analysis of 2024–2025 data shows that the “learning curve” is being automated, leaving early-career professionals stranded between AI agents and senior incumbents.

There are three drivers behind this phenomenon.

First, it’s the ROI problem. AI agents are mastering the grunt work (code generation, financial modeling)¹ that juniors used to learn on. Companies no longer have a financial incentive to pay for the “ramp-up” period.

Then, there’s the “gray ceiling.” Older cohorts are delaying retirement and retaining senior roles³, creating a demographic blockade that stops upward mobility.

Finally, the whole concept of “entry-level” is now a misnomer. Postings now routinely demand 2–3 years of experience, creating a paradox where you need the job to get the job.⁶

While the ladder is broken, a new model is forming. We track the rise of “AI Apprenticeships” and “Superagency,” a trend where juniors use AI to bypass the skills gap and perform at mid-level capacity.

More on all that below.

The Macroeconomic Landscape: From “Great Freeze” to “Fragile Thaw”

The Stagnation of Hiring Intent in 2025

The context for the current crisis in entry-level employment is a broader cooling of the global labor market, a phase often characterized by economists as the “Great Freeze,” followed by a hesitant, uneven thaw. 

Data from the National Association of Colleges and Employers (NACE) regarding the Class of 2026 provides a baseline for this environment. Employers project a marginal 1.6% increase in hiring for the Class of 2026 compared to the Class of 2025.⁹ In real terms, when adjusted for the increasing number of graduates and the expanding labor supply, this “flat” projection signals a functional contraction in opportunity. It is a market focused on efficiency, retention, and cost containment rather than expansionary investment in human capital.

This caution is further reflected in employer sentiment. A plurality of employers, approximately 45%, now characterize the overall job market for new graduates as merely “fair,” a significant downgrade from previous years when ratings of “good” or “excellent” were common.¹¹ 

This metric is an indicator of organizational confidence: when employers rate the market as “fair,” they are signaling a reluctance to commit to the long-term training cycles associated with junior hires.

The pullback is evident in aggregate hiring volumes as well:

• LinkedIn’s workforce data from late 2025 shows national hiring slowing by nearly 9% year-to-date, remaining over 20% below pre-pandemic levels (benchmarked against 2019).¹² 
• This suggests that the post-pandemic “correction”—the shedding of excess headcount hired during the 2021–2022 boom—has not merely stabilized but has established a new, lower baseline of activity. 
• The “Great Freeze” has fundamentally altered the velocity of the labor market, with the hiring rate across all industries hovering near decade lows.³

The “Low-Hiring, Low-Firing” Equilibrium

A critical nuance of the 2025 labor market is the emergence of a “low-hiring, low-firing” equilibrium. 

As noted by Federal Reserve Chair Jerome Powell (and later echoed by private sector economists), the economy is driven more by corporate caution than by mass distress.¹³ Layoffs, while high profile in the tech sector, have not spiked systematically across the broader economy. Instead, companies are achieving headcount reduction through attrition—simply choosing not to backfill roles when employees leave.

This dynamic hurts entry-level candidates more than anyone else. In a “low-hiring” environment, the few open roles that do exist are prioritized for immediate impact. Organizations under pressure to maintain margins prefer to hire one experienced senior employee rather than three juniors who require onboarding. 

This structural preference is shown in the shift of recruiting timelines: 

NACE reports a noticeable migration of recruiting activity to the spring, with 37% of full-time hiring now occurring later in the academic cycle, suggesting that employers are delaying decisions until the last possible moment due to economic uncertainty.¹¹

Regional and Sectoral Divergence

The aggregate stagnation masks significant volatility at the sectoral and regional levels. The “missing rung” is not distributed evenly. It is a phenomenon concentrated heavily in the knowledge economy sectors most exposed to AI automation.

Technology and Finance

These sectors, historically the largest consumers of entry-level talent, have pulled back most sharply. The “tech recession” has morphed into a structural realignment. 

In the UK, tech graduate roles fell by 46% in 2024, with projections for a further 53% drop by 2026.¹⁴ Similarly, in the US, entry-level postings in software development and data analysis have plummeted, with some data indicating a 67% decrease in junior tech postings.¹⁴

Healthcare and Government

Conversely, sectors requiring physical presence or regulated certifications remain robust. Healthcare, government, and leisure/hospitality accounted for almost 75% of all jobs added in late 2024 and 2025.³ Healthcare entry-level postings specifically bucked the trend, rising by 13 percentage points.⁶ 

However, these roles often carry different wage trajectories and career paths than the corporate ladder roles that are disappearing.

Geographic Arbitrage

Hiring intensity has shifted away from traditional, high-cost tech hubs. While cities like Dallas-Fort Worth and Denver have seen hiring declines, secondary markets such as Nashville (+6.7%), Detroit (+6.5%), and Atlanta (+4.2%) have shown resilience.¹⁵ This suggests a geographic dispersion of opportunity, forcing early-career workers to look beyond Silicon Valley or New York City to find their first foothold.

The Automation of the Learning Curve: AI as the New Junior

The Displacement of “Codified Knowledge”

The most disruptive force on the entry-level market is the integration of generative AI. 

To understand the “Missing Rung,” it’s crucial to understand the economic purpose of a junior employee. Historically, entry-level jobs consisted largely of tasks that were repetitive, rule-based, and process-heavy: data entry, basic code generation, summarizing meetings, drafting standard communications, and preliminary research. These tasks served a dual economic function:

• They provided necessary, albeit low-value, output for the firm.
• Crucially, they served as a “paid education” or a subsidized learning curve. By performing these rote tasks, the junior employee absorbed “tacit knowledge”—the unwritten rules, cultural context, and complex judgment required for senior roles.¹⁶

In 2025, AI agents have effectively captured the domain of “codified knowledge.” 

McKinsey’s “State of AI in 2025” report reveals that 62% of organizations are experimenting with AI agents, and 23% are scaling agentic systems within at least one business function.¹ 

These agents do not merely “assist” humans: they execute multi-step workflows that used to be the core responsibilities of a junior analyst or developer.

Economic theory on automation (specifically the framework proposed by Acemoglu and Restrepo) suggests that technology displaces labor in tasks that can be standardized.¹⁷ We are witnessing a rapid acceleration of this displacement. Data entry jobs, for example, are predicted to see the largest absolute losses, with 7.5 million roles expected to disappear by 2027.¹⁸ When an AI can generate an SQL query, summarize a legal brief, or debug a code block instantly at near-zero marginal cost, the economic rationale for paying a junior $70,000 to do the same work—even if the human might eventually become a senior—collapses.

The “Drunt Work” Hypothesis and the Missing Ladder

This phenomenon is increasingly referred to as the automation of “drunt work”—the digital grunt work that formerly comprised the bottom rung of the career ladder. As industry commentators note, “the bottom rung of the career ladder simply disappears” during technological shifts, but this time the shift is permanent rather than cyclical.¹⁹

The “learning curve” itself is being automated. Traditional learning curve theory assumes a constant rate of learning, but AI raises the floor of competency required to be valuable.²⁰ 

If an AI operates at the competency of a junior with 2 years of experience, a true novice with 0 years of experience has negative value to the firm—they require supervision and resources without providing output superior to the software.

This reality is reflected in forward-looking hiring plans. A median of 30% of respondents in McKinsey's survey expect a decrease in workforce size within their business functions over the coming year due to AI.¹

Furthermore, in a healthy labor market, “vacancy chains” allow for mobility: a senior leaves, a mid-level moves up, and a junior is hired. AI disrupts this chain by automating the bottom link, severing the pathway for new entrants.

The Productivity Paradox: Displacement vs. Reinstatement

While displacement is the dominant fear, economic theory also predicts a "productivity effect," where the cost savings from automation lead to business expansion and the "reinstatement" of labor in new, higher-value tasks.¹⁷ However, in 2025, we are observing a significant lag between displacement and reinstatement.

Leading Indicators vs. Financial Impact

While 64% of organizations say AI is enabling innovation, only 39% report tangible EBIT (Earnings Before Interest and Taxes) impact at the enterprise level.¹ This suggests that while companies are adopting the technology, they have not yet expanded sufficiently to absorb the labor displaced by it.

The “Implementation” Collapse

The tasks being created are often highly specialized, requiring seniority and judgment (e.g., “AI Orchestrator” or “Killswitch Engineer” ²¹), rather than mass-market entry-level roles. This creates a bottleneck where the new jobs created are inaccessible to the very people displaced from the old ones.

The Gray Ceiling: Demographics and the Stagnation of Mobility

The “Job Hugging” Phenomenon

While AI squeezes the bottom of the ladder, the top is blocked by demographic realities and behavioral shifts among older workers. The “Great Resignation” of 2021–2022 has been replaced by the “Great Stay” or “Job Hugging” in 2025.

This means:

• Quit rates collapse to historic lows. The primary driver of this is the collapse of the “job-hopping premium”—the salary increase gained by switching jobs. In 2022, this premium was nearly 20%; by mid-2025, it had collapsed to just 7%, effectively matching the raises received by employees who stayed.⁴
• People are extremely risk-averse. Economic uncertainty has driven a profound shift in worker psychology. 81% of workers now prioritize job security over salary increases, and 43% state they would accept lower compensation for greater stability.⁴
• This lack of churn has calcified corporate hierarchies. With fewer seniors leaving, fewer mid-level spots open up, which in turn means fewer entry-level slots are created. The vacancy chain has frozen.

The Aging Workforce and “Unretirement”

Simultaneously, the workforce is aging rapidly, creating a “gray ceiling” that physically blocks upward mobility for younger generations.

• The participation rate for older cohorts is rising. Data shows a 24% increase in workers aged 50+ planning to make a job change rather than retire.²² The "silver economy" is not just about consumption; it is about production.
• The divergence in tenure by age is stark. The median tenure for workers aged 55-64 is 9.6 years, while for workers aged 25-34, it is just 2.8 years.⁴
• The normalization of remote and hybrid work has made it easier for older employees to delay retirement. Physically demanding commutes or office environments are no longer barriers, allowing the “Baby Boomer” generation to extend their careers well into their 70s. While this provides economic security for older workers, it creates a zero-sum game for headcount in a “low-hiring” environment.

The Gen Z Tenure Crisis: The 1.1 Year Reality

The result of these forces—the blockage at the top and the erosion at the bottom—is a crisis of tenure for the youngest generation in the workforce.

• Gen Z workers now average just 1.1 years of tenure in their first five years of work. This represents a 38% decrease compared to Millennials at the same career stage.⁶
• Gen Z has the highest attrition rate of any generation, at 22% annually.⁶
• This churn is often mischaracterized as a lack of loyalty. However, the data suggests it is a rational response to the “Vanishing Ladder.” With entry-level roles requiring 0-2 years of experience dropping by 29 percentage points ⁶, and internal promotions blocked by the Gray Ceiling, young workers are forced to move laterally to find any opportunity for advancement.
• Finally, this high turnover creates a feedback loop. Employers are disincentivized from investing in training because they assume the junior will leave in 13 months. This lack of investment forces the junior to leave to find growth, fulfilling the prophecy.

The Death of the “True” Entry-Level Job

The Redefinition of “Entry-Level”

Perhaps the most insidious trend identified in the 2025 data is “experience inflation”—the upward creep of requirements for roles that are nominally entry-level. The term “entry-level” no longer implies “0 years of experience.” Instead, it increasingly serves as a code for “junior pay for mid-level work.”

• Analysis of job postings on platforms like LinkedIn and Indeed reveals that 35% of positions labeled “entry-level” now require years of prior relevant work experience.⁵
• In the software and IT sectors, the situation is even more extreme. More than 60% of entry-level jobs require 3+ years of experience.⁵ Even customer service, traditionally the most accessible rung, now averages a requirement of nearly 2 years of experience.
• This creates a “Paper Ceiling” where graduates are expected to arrive "job-ready" with the skills of a mid-level employee. This expectation is driven by the availability of AI tools; employers assume that with AI, a candidate should be immediately productive, discounting the time required to learn the tools themselves.

The Skills-Based Hiring Paradox

There is a widespread rhetorical shift toward “skills-based hiring,” with 70% of employers claiming to use it.⁹ However, the implementation of this practice reveals a paradox.

• While 50% of employers offer roles with degree equivalency (accepting skills in lieu of a diploma) ⁹, the “skills” required are often verified through prior work experience, not just potential or academic projects.
• Students are often unfamiliar with how to demonstrate these skills in a way that satisfies algorithmic hiring filters. NACE data shows a disconnect: employers want specific “problem-solving examples,” while students rely on academic transcripts.⁹
• To compound the difficulty, there is a surge in demand for AI literacy. 13.3% of entry-level jobs and 10.5% of entry-level job posts now explicitly require AI skills.¹¹ This adds another layer of prerequisites—candidates must not only know the core job function but also the AI tools that automate it.

Sector-Specific Deep Dive: The Tech & Finance Bifurcation

The Software Engineering Crisis

No sector illustrates the “Missing Rung” more aptly than tech. 

The traditional career path—Junior Developer to Mid-Level to Senior—is fracturing under the pressure of AI-generated code.

AI is exceptionally good at the “implementation details”—writing boilerplate code, generating standard functions, and debugging syntax—that junior developers used to handle. This has led to a consensus that the “pure implementation-focused frontend coder” era is drawing to a close.¹⁶

Because of that, the impact on hiring is quantifiable. Entry-level tech job postings have plummeted, with some analyses showing a 67% decrease in US entry-level tech postings between 2023 and 2024.¹⁴ In the UK, tech graduate roles fell by 46% in 2024.¹⁴

Salary Divergence: The Premium on Judgment

This is most visible in compensation trends. We observe a widening gap between junior and senior salaries, reflecting their divergent value in an AI-augmented world. Seniors, who possess the judgment to orchestrate AI, are scarce and valuable. Juniors, whose primary output (code) is now abundant, face wage stagnation.

Table 1: Global Software Engineer Salary Divergence (2025 Estimates)

Source: Compiled from Gini Talent ²³, Ravio ²⁶, and ITWeb ²⁷ data.

As shown in Table 1, the gap in the US is particularly extreme. A senior engineer at a top tech firm can earn nearly triple the salary of an entry-level engineer. In South Africa, the trend is even more explicit: junior salaries declined by 3% in 2024, while senior salaries rose.²⁷ This price signal confirms the "Missing Rung" hypothesis: the market places a diminishing value on the unaugmented junior skillset.

Finance and Professional Services

A similar dynamic is playing out in the financial sector, albeit with different mechanics.

• Tasks such as updating financial models, summarizing earnings calls, and vetting initial data sets are prime targets for AI automation.
• Entry-level finance positions have fallen by 24 percentage points.⁶
• Firms are retraining analysts to use generative AI for faster ideation and report generation.²⁸ The role is shifting from "spreadsheet builder" to "insight generator," effectively raising the bar for entry. An analyst is now expected to do the work that a team of three did in 2020.

The Rise of the “AI Native”

The “Superagency” Counter-Narrative

While the prevailing narrative is one of displacement, a counter-narrative of “Superagency” is emerging, championed by technology leaders who argue that AI actually enhances the value of junior talent—if that talent is “AI Native.”

• Matt Garman, CEO of Amazon Web Services (AWS), has publicly argued that replacing juniors with AI is “one of the dumbest ideas” a company can have.²⁹ His rationale is that junior employees are often the most proficient users of AI tools, having adapted to them during their education or internships.
• This is, to an extent, supported by data. The 2025 Stack Overflow Developer Survey reveals that 55.5% of early-career developers use AI tools daily in their development process, a significantly higher rate than their senior counterparts.²⁹
• Research indicates that over half of Gen Z employees are actively helping senior colleagues upskill in AI.³⁰ This flips the traditional mentorship model on its head: the junior teaches the senior the tool, while the senior teaches the junior the judgment.

The “Editor” Problem

However, this “AI proficiency” creates a new, dangerous bottleneck known as the “Editor Problem.”

• The paradox: to effectively use AI, it’s necessary to evaluate its output. This evaluation requires the “conscientiousness,” context, and deep technical knowledge (“tacit knowledge”) that typically come from years of manual experience.¹⁶
• A junior using AI can produce vast amounts of code or text (“Superagency”), but without the experience to debug or verify it, they risk introducing systemic fragility. They need experience to manage the AI, but they cannot get experience because the AI does the work they used to learn on.
• This is reflected in the trust gap. While 84% of developers use AI tools, 46% distrust the accuracy of the output.¹⁶ The cognitive load of the job has shifted from creation to verification, an inherently senior task.

The Solution Landscape: Apprenticeships and Policy Interventions

From Internship to Registered Apprenticeship

Recognizing the breakdown of the traditional university-to-job pipeline, forward-thinking organizations are pivoting to registered apprenticeships. Unlike internships, which are often short-term and lack structured progression, apprenticeships are long-term, paid training regimens designed to build specific competencies and bridge the experience gap.

And tech apprenticeships have grown 29% over the past four years.³¹

Here are some of the adoption examples from major corporations:

• Accenture: the firm has made a massive structural commitment, with apprentices now making up 20% of their entry-level hiring in North America.³²
• IBM & Microsoft: both have scaled large programs (New Collar, Leap) that focus on skills verification over degree pedigree.³³
• Airbnb: the “Connect” apprenticeship targets non-traditional backgrounds, explicitly excluding computer science graduates to open pathways for self-taught coders.³³

Table 2: Comparison of Major Tech Apprenticeship Programs (2025)

The “AI Apprentice” Curriculum

A new category of apprenticeship is emerging specifically for the AI era. These programs are designed not just to teach coding, but to teach the orchestration of AI systems.

Examples include:

• AI Singapore (AIAP): a globally recognized program that places apprentices on 9-month real-world projects. It focuses on "deep-skilling"—going beyond Jupyter notebooks to deploy production-grade AI systems.³⁴
• Agentic Portfolios: the new requirement for these apprentices is the "Agentic Portfolio." Instead of static code repositories, candidates are building multi-agent systems. Examples include “Travel Planners” using Google’s Agent Development Kit (ADK) or “Startup Validators” using LangGraph.³⁵ These projects demonstrate the ability to make AI agents collaborate—the new core competency of the junior engineer.

Government and Policy Support

Governments are stepping in to subsidize this retraining, recognizing that the “Missing Rung” creates a structural risk of youth unemployment.

Again, a few real-life examples:

• Indiana: the state is integrating AI skills across 900 registered apprenticeship sponsors, aiming to train thousands of workers in “retooled” jobs.³⁷
• El Paso: the “SuperCity AI” apprenticeship is a localized initiative training workers specifically for government AI applications, creating a pipeline of talent for the public sector.³⁸

Policy discussions now focus on “retooled” jobs—occupations where the title remains the same but the skills change entirely due to AI.³⁹ This requires a shift in funding from traditional higher education to continuous, skills-based certification.

Strategic Outlook: The “Seniority Cliff”

The Looming Crisis of Expertise

The most significant second-order insight derived from this analysis is the risk of a “seniority cliff” in the next 5-10 years.

The logic is that seniority is not merely a function of age; it is the accumulation of thousands of solved problems, bugs fixed, and crises averted. If the current generation of juniors never grapples with those low-level problems because AI solves them automatically, they may never develop the deep intuition and “tacit knowledge” required for senior roles.

As a result, if companies stop hiring juniors in 2025, they are effectively eating their own seed corn. By 2030, the industry may face a catastrophic shortage of true senior engineers and leaders—those capable of understanding the system below the AI abstraction layer. We risk creating a generation of “architects” who have never laid a brick.

Future Scenarios (2026–2030)

Based on the current trajectory, we project three potential scenarios for the entry-level market:

The Bifurcated Workforce (Most Likely)

The gap between the “AI-Augmented Elite” (who master agentic workflows and gain entry through competitive apprenticeships) and the “Displaced Middle” (graduates with generic degrees) widens. The elite command massive salaries, while the middle is forced into lower-wage service roles or gig-work data annotation.⁴⁰

The Apprenticeship Renaissance

Corporations and governments successfully scale the apprenticeship model, creating a new, regulated pathway that replaces the “entry-level job” with a “paid residency” model, similar to the medical profession.

The Regulatory Brake

Governments intervene to mandate "human-in-the-loop" requirements for critical sectors, artificially creating demand for junior auditors and verifiers to counter the risks of AI hallucination.

Conclusion

The “Missing Rung” is not a temporary glitch; it is a structural feature of the AI-enabled economy. The era of the “paid learning curve”—where employers subsidized the education of juniors by paying them to do rote work—is over. The new entry-level requires a higher baseline of capability: the ability to orchestrate, verify, and leverage AI from day one.

Rebuilding the ladder requires a coordinated effort. Employers must treat AI as a tool that needs apprentices to manage it.⁴¹ Educators must shift from theory to “Applied AI” and agentic workflows.⁴² And candidates must reinvent themselves not as “coders” or “writers,” but as “AI Orchestrators” and “System Verifiers.” 

The ladder is broken, but a new elevator is being built—for those who can find the buttons.

Appendix: Data Tables and Supporting Statistics

Hiring Intent Projections by Sector (2025–2026)

Resume Keywords for the AI Era (2025)

To navigate the "Experience Inflation" and "AI Literacy" requirements, candidates are optimizing for specific skills.

• Agentic Workflows: Proficiency in tools like LangChain, CrewAI, AutoGen.³⁶
• Data Annotation: Experience with RLHF (Reinforcement Learning from Human Feedback) and Ground Truth verification.⁴⁰
• AI Ethics/Governance: Understanding bias, hallucination risks, and responsible AI deployment.⁴³
• Conscientiousness: The ability to perform deep verification of automated outputs.¹⁹
• System Prompting: Moving beyond basic "prompt engineering" to complex "System Prompt" design and chain-of-thought orchestration.⁴³

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