AI and the Disappearing Entry-Level Job
by Claire Brady, EdD
“If our institutions embrace AI solely to “do more with less,” we risk sending the same message that Knight’s article warns against—that efficiency matters more than people.”
I recently read Will Knight’s piece in Wired, “AI Is Eliminating Jobs for Younger Workers,” and, as often happens when I engage with thoughtful writing on technology, it got me thinking about higher education. The article highlights new Stanford research that provides some of the clearest evidence yet that generative AI is reshaping the workforce. The headline alone is jarring: younger workers, especially in fields like customer service and software development, are already experiencing job displacement because of AI.
But—as Knight notes—the story is more complicated than the headline. The research team, led by Erik Brynjolfsson and colleagues at Stanford, found that AI’s impact is not uniform across the labor market. Younger workers are disproportionately affected, with a 16% decline in employment among 22- to 25-year-olds in certain AI-vulnerable industries. Yet for more experienced workers, the data tells a different story: in many cases, opportunities have remained steady or even grown, particularly where AI is being used to augment rather than replace human expertise.
This nuance matters for higher education leaders. Our students are entering a workforce where the first jobs—the entry-level, often repetitive roles that have traditionally served as launchpads—are increasingly at risk of automation. At the same time, those with deeper experience and specialized knowledge are finding that AI can make them more effective, not less.
So what do we do with this tension?
1) We need to prepare students not just for jobs, but for adaptability.
If the “front door” into certain careers is narrowing, then institutions must think about alternative pathways that help students build experience, confidence, and credibility. Internships, project-based learning, peer mentoring, and real-world partnerships aren’t “nice extras”—they are essential bridges for students who may not have as many entry-level roles waiting for them.
2) We need to shift our teaching and career preparation models from replacement to augmentation
As Brynjolfsson and colleagues argue, the future of work will be increasingly about “centaurs”—humans and machines working in tandem. That means our curricula, advising, and co-curricular programs must help students understand not just how to use AI tools, but how to partner with them thoughtfully while still developing distinctly human skills like critical thinking, ethical reasoning, communication, and empathy.
3) We should model what this looks like as leaders
If our institutions embrace AI solely to “do more with less,” we risk sending the same message that Knight’s article warns against—that efficiency matters more than people. Instead, we have the opportunity to model responsible adoption: leveraging AI to free staff and faculty from administrative burdens so they can spend more time mentoring, teaching, and building community.
4) We need to embrace Higher education’s role play in shaping the broader conversation
Brynjolfsson calls for a national “early warning system” to track how AI impacts employment in real time. Colleges and universities are uniquely positioned to provide that data, amplify those findings, and advocate for policies that ensure AI is used to expand, not contract, opportunity.
AI is not just a technical tool—it’s a force reshaping the very pathways our students will walk. As leaders, we can either allow it to accelerate inequities for the next generation, or we can use it as a catalyst to rethink how we prepare students for lives of purpose, resilience, and leadership.
Read the Wired article: https://www.wired.com/story/stanford-research-ai-replace-jobs-young-workers/?utm_source=flipboard&utm_content=user%2FWIRED
