How the Robot Impact Score is calculated

The Geppetto Robot Impact Score is a composite of four independently sourced data inputs, each normalised to 0 to 100 before weighting is applied. The index reflects both theoretical automation susceptibility and demonstrated real-world deployment, because a robot that exists in a lab is not the same as a robot replacing workers today.

Formula

Score = (Task Automation Research x 0.35) + (Deployment Reality Score x 0.30) + (Academic Consensus Score x 0.20) + (Robot Density Score x 0.15)

Each component is normalised to 0 to 100 before weighting.

Task Automation Research: 35%

Peer-reviewed task-automation research. The Oxford study (Frey and Osborne, 2017) assigns each occupation a probability of automation based on susceptibility to computerisation. This component captures how much of a profession's task load is theoretically automatable. The O*NET-SOC occupation code shown on each profile is a cross-reference for identification only and does not enter the score.

Deployment Reality Score: 30%

The International Federation of Robotics publishes annual World Robotics reports tracking robot installations by sector and application area. This component measures where robot replacement is happening now, not just where it is theoretically possible. Scale: 0 means no commercial deployments recorded, 9 to 10 means hundreds of thousands of units deployed globally. Citation: IFR World Robotics 2025, International Federation of Robotics, Frankfurt.

Academic Consensus Score: 20%

Average of two peer-reviewed methodologies. Frey and Osborne (Oxford, 2017) assign probability scores to 600+ occupations based on susceptibility to computerisation. The OECD task-based approach (Arntz, Gregory and Zierahn, 2016) is more conservative, finding only 9% of jobs at high risk because it analyses task mix within occupations rather than treating them as homogeneous. The range between the two scores is the uncertainty band shown on profession profiles.

Robot Density Score: 15%

The number of commercial robots in the Geppetto directory targeting this profession, divided by the total robots in the relevant category, multiplied by 10. This score updates in real time as new robots are added. Multiple competing commercial products targeting the same profession is a market signal that commercial viability has been established.

Impact Score Bands

80 to 100 (Critical): substantial displacement underway or imminent.
60 to 79 (High): strong automation pressure, multiple vectors.
40 to 59 (Moderate): meaningful task displacement, role evolving.
20 to 39 (Low): limited automation, human judgment dominant.
0 to 19 (Minimal): highly resistant to robot automation.

Limitations

Oxford (Frey and Osborne) scores have faced criticism for overestimating displacement; many occupations predicted to shrink have grown. These scores are one weighted input, not a standalone prediction.
Robot impact does not equal job loss. Acemoglu and Restrepo (MIT, 2022) distinguish displacement effects from reinstatement effects; automation also creates adjacent roles. Geppetto scores measure displacement exposure, not net employment outcomes.
Scores reflect technology available today. Near-term classifications reflect current commercial deployment trajectories, not certainties.
The Robot Density Score reflects robots currently listed in the Geppetto directory. Robots not yet catalogued are not counted.
All 261 scored professions carry an Oxford task-automation score, so none rely on a neutral placeholder for the 35% component.

Citations

Frey, C.B. and Osborne, M.A. (2017). The Future of Employment. Technological Forecasting and Social Change, 114, 254 to 280. University of Oxford.
Arntz, M., Gregory, T. and Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries. OECD Working Paper No. 189.
Acemoglu, D. and Restrepo, P. (2022). Tasks, Automation, and the Rise in US Wage Inequality. Econometrica, 90(5), 1973 to 2016. MIT.
IFR World Robotics 2025. International Federation of Robotics.
O*NET OnLine (2025). U.S. Department of Labor. onetonline.org
Bureau of Labor Statistics (2024). Occupational Employment and Wage Statistics. bls.gov/oes