New reporting argues that workers in AI-exposed roles face uneven returns, and that generative AI can reshape entry-level hiring patterns even while wages rise for experienced workers. A related analysis points to MIT research showing AI systems frequently meet only “minimally sufficient” standards and struggle when tasks require multiple steps, elevated quality, or human-level judgment—constraints relevant to universities training graduates for knowledge work. In parallel, broader labor-market reporting highlights “experience creep,” in which job postings increasingly require more years of experience rather than entry-level fit. The article ties this pattern to AI-enabled productivity expectations, though it notes that proof of direct replacement remains limited. Together, the set suggests institutions need to adjust career outcomes expectations and workforce-aligned curricula—especially for early-career pathways—while also strengthening transparency around how AI changes hiring requirements.