‘AI Impacts’ surveys reveal latest predications on when all jobs will be fully automated

AI researchers project significantly different timelines for machine intelligence and labor automation, according to surveys conducted between 2016-2023 by AI Impacts.

Survey methodology and key definitions: The research focused on two distinct concepts that help frame the future of artificial intelligence development.

• High-Level Machine Intelligence (HLMI) was defined as the point when unaided machines can accomplish every task better and more cheaply than human workers
• Full Automation of Labor (FAOL) represents the milestone when all occupations become fully automatable
• The surveys were conducted across three periods: 2016, 2022, and 2023, tracking shifts in researcher expectations

Key findings and timeline predictions: The data revealed a consistent and substantial gap between expected HLMI and FAOL achievement dates.

• In 2016, researchers predicted HLMI by 2061 and FAOL by 2136, a 75-year gap
• The 2022 survey showed HLMI expected by 2060 and FAOL by 2164, extending the gap to 104 years
• Most recently in 2023, predictions shifted earlier but maintained the gap: HLMI by 2047 and FAOL by 2116

Analysis of the discrepancy: Several potential explanations emerge for the significant timeline differences between HLMI and FAOL.

• Researchers may be approaching these predictions with inconsistent logical frameworks
• The gap could reflect anticipated societal disruptions that would delay widespread automation adoption
• Different interpretations of what constitutes “tasks” versus “occupations” may influence the predictions
• The distinction between technical feasibility and practical implementation could be creating confusion

Broader implications for AGI development: The substantial gap between HLMI and FAOL predictions raises important questions about artificial general intelligence (AGI) timelines.

• The disparity makes it unclear whether AI researchers expect AGI development within this century
• These inconsistencies highlight the challenge of making accurate long-term predictions about AI capabilities
• The varying interpretations suggest a need for more precise definitions in AI forecasting

Future uncertainty and research implications: The significant timeline gaps between HLMI and FAOL achievement highlight fundamental uncertainties in AI development forecasting, suggesting that even experts struggle to form consistent views about the future of artificial intelligence and its implementation across society.