The growing use of generative AI in the workplace raises a paradox for entry-level workers. The very tasks that once trained new workers—such as summarizing meetings, cleaning data, and drafting memos—are increasingly automated. This means that entry-level jobs today require experience that entry-level roles no longer supply.
AI has cannibalized the routine, low-risk work tasks that used to teach newcomers how to operate in complex organizations. Without those task rungs, the climb up the opportunity ladder into better employment options becomes steeper—and for many, impossible. This is not a temporary glitch. AI is reorganizing work, reshaping what knowledge and skills matter, and redefining how people are expected to acquire them.
The consequences ripple from individual career starts to the broader American promise of economic and social mobility, which includes both financial wealth and social wealth that comes from the networks and relationships we build. Yet the same technology that complicates the first job can help us reinvent how experience is earned, validated, and scaled. If we use AI to widen—not narrow—access to education, training, and proof of knowledge and skill, we can build a stronger career ladder to the middle class and beyond. A key part of doing this is a redesign of education, training, and hiring infrastructure.
In the words of Burning Glass Institute President Matt Sigelman, “AI doesn’t automate away jobs. It automates tasks. Whether that opens time to take on more valuable tasks, whether new efficiencies unlock latent demand that actually grows opportunity, or whether employers decide to take the savings depends on a range of factors and plays out over time… First, we need an accessible infrastructure.”
