A New AI Career Ladder — from ssir.org (Stanford Social Innovation Review) by Bruno V. Manno; via Matt Tower
The changing nature of jobs means workers need new education and training infrastructure to match.
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.
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What’s needed is a redesigned model that treats work as a primary venue for learning, validates capability with evidence, and helps people keep climbing after their first job. Here are ten design principles for a reinvented education and training infrastructure for the AI era.
- Create hybrid institutions that erase boundaries. …
- Make work-based learning the default, not the exception. …
- Create skill adjacencies to speed transitions. …
- Place performance-based hiring at the core. …
- Ongoing supports and post-placement mobility. …
- Portable, machine-readable credentials with proof attached. …
- …plus several more…