Theory vs Practice
Where does real-world AI usage diverge most from theoretical exposure? Ranked by the absolute gap between Anthropic's observed AI usage percentile and the theoretical AIOE percentile. Red rows = usage exceeds theory. Blue rows = theory exceeds usage.
| # | Occupation | Gap (pts) | Direction | AIOE Theory | AI Exposure Rank | Exposure | Likely impact |
|---|---|---|---|---|---|---|---|
| 1 | Building painter 71311 | +75 | Above theory | -57% | 40/100 | Moderate | Mixed pathway |
| 2 | Audiologist 22661 | -65 | Below theory | 115% | 41/100 | Moderate | Mixed pathway |
| 3 | Speech therapist 22662 | -65 | Below theory | 115% | 41/100 | Moderate | Mixed pathway |
| 4 | Library officer 34331 | -64 | Below theory | 112% | 54/100 | Moderate | Mixed pathway |
| 5 | Website administrator/Webmaster 35140 | +60 | Above theory | -3% | 83/100 | Very High | Substitution-led |
| 6 | Environmental officer (environmental protection) 21331 | -59 | Below theory | 136% | 56/100 | Moderate | Mixed pathway |
| 7 | IT Infrastructure technician 35121 | +59 | Above theory | -3% | 76/100 | High | Mixed pathway |
| 8 | IT security technician 35122 | +59 | Above theory | -3% | 76/100 | High | Mixed pathway |
| 9 | IT support technician (including IT user helpdesk technician) 35123 | +59 | Above theory | -3% | 76/100 | High | Mixed pathway |
| 10 | Landscape architect 21621 | -59 | Below theory | 103% | 48/100 | Moderate | Mixed pathway |
| 11 | Automation engineer (including robotics engineer) 21413 | -56 | Below theory | 128% | 58/100 | Moderate | Mixed pathway |
| 12 | Manufacturing engineer 21411 | -56 | Below theory | 128% | 58/100 | Moderate | Mixed pathway |
| 13 | Process engineer 21415 | -56 | Below theory | 128% | 58/100 | Moderate | Augmentation-led |
| 14 | Production engineer 21412 | -56 | Below theory | 128% | 58/100 | Moderate | Mixed pathway |
| 15 | Quality control/assurance engineer 21414 | -56 | Below theory | 128% | 58/100 | Moderate | Mixed pathway |
| 16 | Chemical engineering technician 31161 | +51 | Above theory | 6% | 53/100 | Moderate | Mixed pathway |
| 17 | Chemical engineering technician (petrochemicals) 31163 | +51 | Above theory | 6% | 53/100 | Moderate | Mixed pathway |
| 18 | Environmental engineer 21430 | -50 | Below theory | 134% | 70/100 | High | Augmentation-led |
| 19 | Sales supervisor 52201 | +48 | Above theory | 5% | 59/100 | Moderate | Mixed pathway |
| 20 | Shop sales assistant 52202 | +48 | Above theory | 5% | 59/100 | Moderate | Mixed pathway |
| 21 | Photographer 34310 | +48 | Above theory | -17% | 53/100 | Moderate | Mixed pathway |
| 22 | Data entry clerk 41320 | +48 | Above theory | 47% | 92/100 | Very High | Mixed pathway |
| 23 | Data processing control clerk 43151 | +48 | Above theory | 47% | 86/100 | Very High | Mixed pathway |
| 24 | Travel consultant/Reservation executive 42210 | +43 | Above theory | -21% | 57/100 | Moderate | Mixed pathway |
| 25 | Cabin attendant/steward 51112 | +43 | Above theory | -21% | 37/100 | Low | Limited direct change |
Gap = Anthropic observed usage percentile minus theoretical AIOE percentile. Positive means more AI adoption than theory predicts. Learn more
Frequently asked questions
Where does AI theory diverge from actual usage?
Academic AI exposure indices measure theoretical task automation potential, while Anthropic's observed usage data shows what people actually use AI for. The biggest gaps reveal where adoption lags or leads predictions.
Why do some jobs have high theoretical AI exposure but low real usage?
Regulatory barriers, trust requirements, or workflow integration costs can slow adoption even when tasks are technically automatable. Conversely, some low-exposure roles adopt AI tools faster than predicted.