The On-Ramp Problem: Navigating AI Automation and Human Augmentation
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The On-Ramp Problem: Navigating AI Automation and Human Augmentation
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Welcome to The Other AI. In this deep dive episode, we explore "The On-Ramp Problem," based on the research of Basil C. Puglisi. While headline AI job numbers appear stable, cutting the data by age reveals a sharp contraction in early-career employment for highly AI-exposed roles. We discuss how AI is rapidly absorbing the junior tasks—like retrieving, summarizing, and formatting—that traditionally served as the "on-ramp" for workers to learn their jobs.
Join us as we unpack the critical choice between automation and augmentation, the financial pressures pushing companies toward cheaper replacement paths, and insights from Erik Brynjolfsson's Canaries Dashboard. We also look at the operational answer to this crisis: using person-scale measurements like the Human Enhancement Quotient (HEQ) and Augmented Intelligence Score (AIS) to ensure humans are actively grown through AI collaboration at governed checkpoints, rather than simply replaced.
If we can only fix what we can measure, are we measuring the right thing, or only counting the jobs after they are already gone?