Where to Draw the Line on AI Autonomy

Public acceptance versus worker desire, on one map

2025-10-22

Where to Draw the Line on AI Autonomy

AI adoption is not just about technology. It's about understanding the delicate balance between public moral acceptance and worker desire.

This AI Deployment Map is a reminder of where to draw the line between autonomy, co-pilot assistance, and purely assistive functions.

Where do you see your team applying AI for the greatest impact?

The evidence behind the map. Three excerpts from Friis and Riley, reproduced for commentary with credit to the authors.

Figure 4: moral repugnance versus technical feasibility, one point per occupation
Figure 4 from Friis and Riley. Upper right is the moral-friction zone: AI is capable but socially resisted. Lower left is latent: not yet feasible but broadly accepted.
Table S12: example occupations by technical suitability and moral repugnance
Table S12 from Friis and Riley. Search marketing strategists score 1.00 on technical suitability and 2.31 on repugnance. School psychologists score 0.79 and 5.45.
Abstract of Performance or Principle
The abstract. The line that matters: the public already supports automating 30% of occupations, and support nearly doubles to 58% when AI is described as outperforming humans at lower cost. A 12% subset stays off-limits on principle.

Sources. The map draws on the Stanford SALT Lab study Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce (Shao, Zope, Jiang, Pei, and colleagues, 2025), its companion site futureofwork.saltlab.stanford.edu, the Harvard Business School working paper by Simon Friis and James Riley, Performance or Principle: Resistance to Artificial Intelligence in the U.S. Labor Market, whose figures appear below, and Ethan Mollick's thread that brought it to my feed.

First published on LinkedIn, 22 October 2025. Lightly revised. Original post.