Our chart this week comes from “Some Simple Economics of AGI,” a new paper by MIT’s Christian Catalini, Washington University’s Xiang Hui, and UCLA’s Jane Wu. Rather than breaking jobs into component tasks and asking which ones AI can do (the standard approach from McKinsey to Goldman Sachs), they plot work across two axes: the cost to automate it, and the cost for a human to verify the output is correct.
This creates four quadrants. Q1, the Safe Industrial Zone, covers work that’s cheap to automate and cheap to check: data entry, basic coding, routine financial processing. Q2, the Runaway Risk Zone, is where things get interesting: work AI can do cheaply but that’s expensive for humans to verify properly, like legal drafting, medical diagnosis, or complex software development. Q3, the Artisan Zone, captures work that’s hard to automate but easy to verify: plumbing, electrical work, physiotherapy. And Q4, the Pure Tacit Zone, covers work that’s both hard to automate and hard to verify: leadership, novel research, complex negotiation.
This MIT paper offers a different way to think about AI and work, not through the task automation lens we’re used to, but through the gap between what AI can produce and what humans can meaningfully check.