Why AI Could Be a Boon for Workers

A recent article in the New York Times seemed to signal an AI-fueled apocalypse for job seekers. The article profiled the plight of recent college graduates who’d expected six-figure jobs with their computer science degrees but were now scrapping for shifts at Chipotle. According to one expert quoted in the Times, the jobs “most likely to be automated are the entry-level positions that [recent graduates] would be seeking.”  

Recent research shows that AI is replacing entry-level jobs, similar to how mechanical automation eliminated low-skill manufacturing roles in past decades. However, this expanding definition of “expertise” will eventually create new jobs and pathways for workers to gain skills necessary to stay competitive in a post-AI era. The outcome could be the democratization of expertise and wider opportunities for upward mobility.  

AI Redefines Expertise 

David Autor, an MIT economist, argues that AI changes the nature of jobs by automating routine tasks, which “allows people with less formal education to perform more complex tasks.”  

Instead of hollowing out the labor market, AI could create more opportunity and upward mobility. Expertise has long been equated with diplomas, degrees, and seniority. But what if it meant the ability to receive immediate feedback and learn on the fly? Autor calls it a “worker complementary technology.” For example, it could make a nurse more efficient, a technician more proficient, a teacher more engaging.  

Let’s consider teaching. AI-powered tools can analyze student answers on a quiz during class and pinpoint concepts and issues that students don’t understand. This feedback helps teachers adjust the explanations in real time instead of waiting until papers are graded manually. Teachers can also upload their lesson plans to an AI workspace and compare them. The AI tool can then point out potential gaps and overlaps and suggest a “best of” version, reducing the time teachers spend in planning meetings.  

An April 2025 report based on a poll of over 2,000 teachers by Gallup and the Walton Family Foundation examined how AI affects their teaching. It found that six out of 10 teachers have used an AI tool for their work this school year, with about three out of 10 using it at least weekly. Those teachers who use AI weekly save 5.9 hours per week, the equivalent of six weeks per school year. Finally, a majority of teachers using AI tools say they enhance the quality of their work, including higher-quality insights about student learning (61 percent) and improved quality in grading and student feedback (57 percent).  

Matt Seigelman and his Burning Glass Institute colleagues introduce the notions of “learning curves” and “earning curves” to suggest how AI changes the value of workplace expertise. Learning curves refer to the time and complexity required to master a given role.  

Some jobs have steep learning curves over extended periods, which they call growth role occupations. They include jobs like marketing associate, junior project manager, and paralegals.  

Other jobs have front-loaded learning curves that require more initial knowledge, which are called mastery role occupations. These include jobs like network administrator, data analyst, and computer-aided design technician. Mastery roles tend to offer fewer growth opportunities because of their upfront learning curves. Earning curves show how wages increase as workers gain experience. Ideally, learning and earning curves are aligned.  

Sigelman and his colleagues argue that as AI handles repetitive tasks, the traditional early-career, entry-level growth roles that helped employees climb the career ladder are disappearing. What remains are mastery roles, which require experience and high-level oversight but don’t easily accommodate newcomers.  

So fewer people can access the roles where they might gain essential skills, creating an experience trap, especially for new and young workers. 

The Experience Trap 

So, AI eliminates bottom-rung jobs where young workers gain experience, creating an experience trap. “Everyone wants to hire somebody with three years’ experience, and nobody wants to give them three years’ experience,” says Peter Cappelli, a professor of management at The Wharton School. For example, OpenAI researchers have documented how ChatGPT can now perform tasks across more than 1,000 occupations, as classified by the U.S. Department of Labor. A report by Oxford Economics describes how employers are reducing entry-level roles for college degree holders, often assuming that AI can now handle the grunt work. “While some of it is related to a normalization after the post-pandemic surge,” writes Oxford’s senior U.S. economist Matthew Martin, “there are signs that entry-level positions [for degree holders] are being displaced by artificial intelligence at higher rates.”  

A Wall Street Journal analysis makes this point using data from The Burning Glass Institute, a labor analytics firm. The national unemployment rate is 6.6 percent for 22- to 27-year-old college graduates with a bachelor’s degree or higher over the last 12 months ending in May, compared to the national unemployment rate of around 4 percent.  

The experience trap is no longer primarily affecting the tech sector. In 2019, 61 percent of AI-related job postings were in the IT and computer science fields, with 39 percent in non-tech sectors, according to labor analytics firm Lightcast. By 2024, the majority of AI-related job postings (51 percent versus 49 percent) were outside the tech sector.  

These developments call into question the long-standing education-to-employment road: study hard, earn a certificate or college degree, secure a starter job, and advance in the worker ranks. That model assumed there would always be a first step on the ladder. But AI is eliminating that step. 

A New Kind of Ladder 

AI raises the bar for demonstrated expertise while lowering barriers to acquiring it. It can function as a tutor, mentor, coach, and assistant. AI can help a 20-year-old write a more compelling cover letter, assist a mid-career worker in learning Python, or guide a high school graduate in building a digital marketing campaign. 

AI can help validate skills through simulations, project-based assessments, and portfolios. A welder can train using virtual reality. A data analyst can build dashboards using real-world datasets and ChatGPT. A designer can showcase AI-generated visuals using tools like Adobe Firefly.  

In effect, AI allows individuals to demonstrate experience, making traditional credentials such as high school diplomas, college degrees, and internships less critical. Autor says, “I think we’ll know we’re being successful if we see more people without four-year college degrees—which only a third of Americans have—doing high-value work in education, in healthcare, in design, in repair, et cetera.”  

If employers use AI to double down on exclusive practices—such as relying heavily on degrees—they will reinforce inequality. But if they use AI to identify hidden talent, it can dramatically expand opportunity. Byron Auguste of Opportunity@Work and Papia Debroy of Brookings write, “AI…amplifies the goals—good or bad—of those who deploy it.” 

The Road Ahead  

Is there a remedy to the Catch-22 in which young job seekers need experience to get hired, but need a job to gain it? A promising answer can be found in an approach to workforce preparation with a long and rich history, from ancient times to the modern era: apprenticeships. Unlike unpaid internships or classroom-only learning, apprenticeships develop expertise by embedding education and training directly in paid employment, offering wages, mentorship, and progressive responsibility.  

Apprenticeships are an earn-and-learn model: apprentices earn while learning, gaining precisely the kind of hands-on experience employers say they want. By the end of an apprenticeship, the “entry-level” paradox is resolved since the apprentice is no longer inexperienced. Instead, the apprentice has the skills and track record of a productive worker. They make experience part of the first job rather than a barrier to getting it.  

Apprenticeships also address the critical equity dimension of the experience gap, which is most severe for young people who cannot afford unpaid internships or years of debt-financed schooling. Apprenticeships democratize opportunity by paying from day one and tying advancement to demonstrated skills. In Apprentice Nation, Ryan Craig says apprenticeships “represent the best way to rebuild the rungs of the career ladder” and ensure that experience is not a privilege of the few but a pathway open to all. 

Apprenticeships democratize expertise and opportunity by letting young people earn while they learn, transforming experience from a barrier into a bridge.