I. Executive Summary

Artificial intelligence is reshaping the U.S. labor market, with recent advances in generative and cognitive AI disproportionately affecting office-based, analytical, and entry-level jobs. These occupations, including administrative support, customer service, marketing, and junior analytical roles, are heavily concentrated among Gen Z workers entering the workforce.

Unlike previous waves of automation that primarily displaced manual labor, AI is now altering traditional pathways into stable careers by reducing demand for early-career workers and compressing wages in AI-exposed occupations. Existing workforce and training systems were not designed to address this type of gradual, task-level displacement, leaving young workers particularly vulnerable to income instability and skill erosion.

This brief argues that AI represents a structural transition in how early-career labor markets function. It proposes a labor-centered policy framework that classifies AI systems by their employment effects, provides portable wage insurance and reskilling support, and incentivizes shared employer training. Together, these policies aim to align technological innovation with career mobility and long-term wage growth for Gen Z.

II. Relevance & Background

Artificial intelligence is rapidly transforming the U.S. labor market, and emerging research shows that its effects are falling unevenly across age groups. A joint analysis by the Brookings Institution and the Stanford Digital Economy Lab finds that over 30 percent of U.S. workers could see at least half of their job tasks affected by generative AI, with exposure concentrated in white-collar and entry-level occupations rather than in traditional manufacturing or manual labor. These roles include administrative support, customer service, entry-level technical positions, and marketing, all of which disproportionately employ recent college graduates and Gen-Z workers.

Evidence suggests that these changes are already altering early-career employment outcomes. A Stanford Digital Economy Lab study reports that employment among workers aged 22-25 in highly AI-exposed occupations has declined relative to older cohorts, indicating that firms are beginning to substitute AI for junior labor in tasks that historically served as entry points into professional careers. As a result, AI is weakening the traditional on-ramps through which young workers build skills, gain experience, and progress to higher-wage roles. 

Unfortunately, reskilling and workforce development programs are not keeping up with the speed of technological disruption across industries. Over half of Gen-Z workers worry about being replaced by colleagues with stronger AI skills, reflecting genuine concern about their longer-term career prospects.  Current AI policy frameworks have focused on governance, while labor protections and training mechanisms remain underdeveloped. Without targeted policy intervention, gains from AI adoption will primarily benefit companies rather than workers, increasing wage inequality, accelerating skill obsolescence, and creating economic uncertainty for a generation already facing high costs of living and other financial challenges.

III. Tried Policy

In the United States, federal responses to artificial intelligence have largely centered on technological safety, ethical use, and national competitiveness rather than labor market protections. A key example of this is the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (2023), which instructs federal agencies to focus on addressing algorithmic bias, data privacy, cybersecurity risks, and national security concerns. While this executive order does acknowledge that AI may affect workers, it does not establish any mechanisms to protect wages, guarantee retraining, or support employment transitions for those that are impacted by automation.

Similarly, federal investments in AI through agencies such as the National Science Foundation and the Department of Commerce emphasize funding for research, standards development, and national innovation capacity. These efforts mainly focus on preventing misuse and ensuring responsible development but largely assume that labor market adjustments will be handled indirectly by existing workforce systems. As a result, there is no federal framework that explicitly links AI-driven productivity to worker compensation, restraining guarantees, or income stabilization for those that are displaced by automation, leaving workers in early careers particularly exposed.  

International regulatory efforts demonstrate similar limitations. The European Union’s Artificial Intelligence Act (AI Act), the first legal framework on AI, establishes a risk based classification system that categorizes AI systems as minimal, limited, high, or unacceptable risk. While this framework helps create more consumer protection and transparency, particularly in areas involving automated decision-making, the AI Act does not incorporate any labor market protections tied to AI adoption. When an AI system is labeled as high risk, the law places obligations on the companies that build or deploy the AI system itself, rather than on employer obligations related to wages, retraining, or job transitions. Even in one of the world’s most comprehensive AI regulatory frameworks, workers affected by task automation receive no automatic wage protection, retraining support, or transition assistance. 

While governments have largely relied on already existing workforce development programs to address automation related disruption, these initiatives were in no way designed for the pace or structure of AI-driven change. In the United States, workforce training efforts are fragmented across federal and state systems under longstanding workforce development frameworks, such as the Workforce Innovation and Opportunity Act (WIOA). While the WIOA supports displaced workers, eligibility requirements, local variation, and limited funding limit its impact, especially for younger workers who might not have yet qualified as “dislocated.” In addition, many of the current upskilling programs are specific to only one firm, which means that skills and credentials aren’t easily transferable across multiple jobs. For Gen Z workers, who are more likely to change employers very early in their careers, training benefits often do not travel with them, reducing their benefits. 

IV. Policy Solutions

The integration of AI into the labor market presents both displacement risks and opportunity gaps for Gen Z workers entering their careers. While AI adoption promises productivity gains, its uneven impact across sectors and skill levels threatens to exacerbate existing inequalities for younger workers with less established career capital. Effective policy must balance innovation incentives with worker protections, creating pathways for adaptation rather than simply cushioning the fall. A comprehensive approach requires classification systems that distinguish between complementary and substitutive AI applications, portable safety nets that follow workers across jobs, mechanisms to distribute training costs more equitably, and proactive career guidance infrastructure. 

A. Establish a Labor Impact Classification System for AI Deployment

Policymakers should develop a mandatory classification framework that categorizes AI systems by their labor market effects, distinguishing between augmentative technologies that enhance worker productivity, substitutive systems that automate tasks, and transformative applications that restructure entire occupations. This classification would trigger differentiated regulatory responses: augmentative AI would receive tax incentives, substitutive AI would require advance notice periods and transition support funding, and transformative AI would mandate impact assessments before deployment. The framework should be administered through a joint industry-labor commission with sector-specific expertise, modeled on environmental impact review processes. By creating transparency around AI’s employment effects, this system would give workers, firms, and training providers the advance warning needed to prepare for transitions while encouraging investment in human-complementary technologies. The system would be administered by the Department of Labor in coordination with sector-specific boards, and firms would be required to file labor-impact disclosures before deploying large-scale substitutive or transformative AI systems.

B. Create Portable Wage Insurance and Reskilling Voucher Accounts

Traditional unemployment insurance is ill-suited for the gradual displacement and wage erosion AI may cause. Congress should establish Individual Transition Accounts–portable, federally-funding accounts that provide displaced workers with wage insurance equal to 50 percent of the difference between a worker’s previous wage and their new, lower wage for up to two years, plus $8,000 in reskilling vouchers redeemable at certified training providers, community colleges, or apprenticeship programs. Unlike traditional UI, those accounts would activate even when workers remain employed but face significant wage reductions due to AI-driven skill devaluation. The program would be funded through a modest payroll tax on firms above 500 employees deploying substitutive AI systems. This approach acknowledges that AI displacement often manifests as underemployment rather than joblessness, while giving Gen Z workers the financial runway to acquire new skills without falling into debt or poverty traps. 

C. Incentivize Employer Consortia for Shared Training Infrastructure

Market failures prevent individual firms from investing adequately in training when workers can be poached by competitors. Federal policy should offer substantial tax credits (up to 40% of training costs) to employer consortia that jointly fund industry-specific training programs, with bonus credits for including Gen Z apprenticeships and pathways from community college. These consortia would share both costs and trained workers, reducing poaching concerns while creating standardized credential pathways. The model builds on successful sector partnerships in healthcare and advanced manufacturing, but scales them nationally with dedicated funding for AI-adjacent skills–data literacy, human-AI collaboration, and adaptive problem-solving. By pooling resources, firms can offer Gen Z workers more comprehensive training than any single employer could justify, while maintaining the employer connection that makes training relevant and leads to actual job placement. 

D. Establish an AI Career Navigation Service for Young Workers

Gen Z workers face unprecedented uncertainty about which skills and occupations will remain valuable as AI evolves. The Department of Labor should create a national AI Career Navigator program, providing free one-on-one counseling and sophisticated labor market analytics to workers under 30. This service would use real-time job posting data, AI adoption patterns, and skills adjacency mapping to help individuals identify viable career pivots before their current roles are automated. The program would be delivered through partnerships with libraries, community colleges, and workforce development boards, with dedicated mobile apps providing on-demand guidance. Unlike generic career counseling, this service would specifically track AI’s sectoral spread and help Gen Z workers position themselves in complementary rather than competing roles. Early intervention–helping workers adjust their trajectories before displacement–is far more cost-effective than remediation after job loss, and would give younger workers the agency to navigate disruption proactively.

V. Policy Problem

AI is rapidly changing entry-level work, yet U.S. labor policy provides no framework for distinguishing between AI that enhances workers and AI that replaces them. This creates a negative externality: firms capture the productivity gains from automation, while workers bear the costs through job loss, wage compression, and skill erosion. As a result, privately efficient AI adoption can be socially inefficient, especially when it undermines long-term workforce development.

These dynamics are most harmful for Gen Z workers, who enter the labor market with limited bargaining power and little accumulated firm-specific capital. When AI compresses entry-level wages or eliminates junior roles, young workers lose critical opportunities to build human capital, leading to flatter career trajectories and lower lifetime earnings.

Gen Z is particularly exposed, as many work in jobs that are most likely to be automated, such as administrative support, customer service, content work, and junior analytics. These jobs are important in developing human capital. When they disappear or are de-skilled, the economy is exposed to dynamic inefficiency, where underinvestment in young workers today could lead to lower future productivity and mobility.

Reskilling has the effect of a public good, but training remains fragmented and employer-linked. This leads to underinvestment and adverse selection, where those who need training the most find it hard to access it. Existing programs are focused on unemployment, despite the fact that AI often causes gradual displacement and obsolescence of skills from one job to another.

Existing policy has the effect of increasing innovation and burdening young workers with the costs of adjustment. Unless action is taken to internalize externalities, correct market failures, and share gains more broadly, AI threatens to further entrench inequality and undermine Gen Z's entry to the labor market.

VI. Youth Impact

For Gen-Z, the impact of artificial intelligence is an immediate economic reality. AI is reshaping entry-level employment. Without wage protections or accessible reskilling pathways, Gen-Z workers may be forced into lower-quality employment, increasing debt burdens and delaying milestones such as homeownership, savings accumulation, and family formation. 

AI-driven disruption is not inherently harmful if paired with supportive labor policy. While many young workers will face automation, their ability to adapt will depend on adequate training opportunities. Policies that classify AI by labor impact and expand access to education and transferable reskilling support can help Gen-Z transition into AI-augmented roles rather than being displaced by them. Ensuring that young workers share in AI’s productivity gains is essential for sustaining economic mobility and the long-term health of any economy.

VII. Conclusion

Artificial intelligence is reshaping labor market entry in ways that have significant implications for long-term economic mobility and productivity. For Gen Z, AI-driven task automation and job restructuring reduce opportunities for skill accumulation at early career stages, increasing the likelihood of wage stagnation and persistent inequality. When entry-level pathways weaken, the resulting effects extend beyond individual workers, lowering aggregate human capital formation and future productivity growth.

Current policy approaches do not adequately address these dynamics. By emphasizing AI governance and innovation while relying on legacy workforce systems, policymakers have left key labor market externalities unaddressed. Firms capture a disproportionate share of productivity gains, while workers, particularly those early in their careers, bear the costs of adjustment through lower wages, skill obsolescence, and employment instability. This outcome reflects a market failure in which privately efficient automation produces socially inefficient labor market outcomes.

Targeted labor policy can mitigate these risks without constraining innovation. Classifying AI systems by their labor market effects, expanding access to portable wage insurance and reskilling support, and encouraging collective investment in training would internalize adjustment costs and better distribute productivity gains. These interventions recognize that AI-driven disruption is gradual and uneven, and that early-career workers require distinct forms of protection and support.

If properly designed, labor-centered AI policy can preserve innovation while preventing early-career collapse. Classifying AI by labor impact, pairing automation with portable wage insurance and reskilling, and requiring collective investment in training would turn AI from a purely cost-cutting tool into a system that co-invests in human capital. For Gen Z, this means AI becomes a pathway to higher productivity and better jobs rather than being a barrier to entering the middle class.

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