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AI Timeline Surveys: A Comparative Analysis (1972–2016)
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https://aiimpacts.org/author/katja/
Credibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: AI Impacts
Produced by AI Impacts, this resource is frequently cited in discussions about AI forecasting reliability and is useful background for anyone evaluating current claims about AGI timelines or assessing expert consensus on transformative AI development.
Metadata
Importance: 62/100organizational reportanalysis
Summary
A meta-analysis of twelve expert surveys on AI timelines spanning 1972 to 2016, examining predictions about when human-level AI might be achieved. The analysis highlights wide variation in median estimates (2020s to 2085) and significant methodological differences across surveys. This resource helps contextualize expert uncertainty and disagreement about transformative AI development timelines.
Key Points
- •Covers twelve distinct AI timeline surveys from 1972 to 2016, providing a longitudinal view of expert forecasting on human-level AI.
- •Median estimates for human-level AI vary dramatically across surveys, ranging from the 2020s to 2085, reflecting deep uncertainty.
- •Surveys differ substantially in methodology, respondent selection, and definitions of 'human-level AI,' making direct comparisons difficult.
- •Highlights persistent overconfidence and short-termism in some predictions, as well as the challenge of defining meaningful AI milestones.
- •Useful reference for understanding the historical track record of AI forecasting and informing current predictions about transformative AI.
Review
The AI Impacts Survey provides a critical meta-analysis of expert predictions regarding the development of human-level artificial intelligence, synthesizing results from twelve different surveys conducted between 1972 and 2016. The research highlights significant methodological variations, including differences in participant backgrounds, survey framing, and definitions of 'human-level AI', which contribute to the wide range of predicted timelines.
Key methodological insights include potential bias from AGI researchers who may be overly optimistic, the impact of 'inside' versus 'outside' view estimation approaches, and the challenge of consistently defining human-level AI. The surveys predominantly feature AI researchers, conference attendees, and technical experts, with median estimates for a 10% chance of human-level AI clustering in the 2020s and 50% chance estimates ranging between 2035 and 2050. This comprehensive review underscores the uncertainty and complexity of predicting technological breakthroughs, emphasizing the need for nuanced, multidisciplinary approaches to forecasting transformative AI capabilities.
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AI Timeline Surveys – AI Impacts
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Published 10 January 2015
We know of twelve surveys on the predicted timing of human-level AI. If we collapse a few slightly different meanings of ‘human-level AI’, then:
Median estimates for when there will be a 10% chance of human-level AI are all in the 2020s (from seven surveys), except for the 2016 ESPAI , which found median estimates ranging from 2013 to long after 2066, depending on question framing .
Median estimates for when there will be a 50% chance of human-level AI range between 2035 and 2050 (from seven surveys), except for the 2016 ESPAI , which found median estimates ranging from 2056 to at least 2106, depending on question framing.
Of three surveys in recent decades asking for predictions but not probabilities, two produced median estimates of when human-level AI will arrive in the 2050s, and one in 2085.
Participants appear to mostly be experts in AI or related areas, but with a large contingent of others. Several groups of survey participants seem likely over-represent people who are especially optimistic about human-level AI being achieved soon.
Contents
Details
List of surveys
These are the surveys that we know of on timelines to human-level AI:
Michie (1972)
Bainbridge (2005)
AI@50 (2006)
Klein (2007)
AGI-09 (2009)
FHI Winter Intelligence (2011)
Kruel (2011-12)
Hanson (2012 onwards)
Müller and Bostrom : AGI-12, TOP100, EETN, PTAI (2012-2013)
Results
Results summary
Year
Survey
#
10%
50%
90%
Other key ‘Predictions’
Participants
Response rate
Link to original document
1972
Michie
67
Median 50y (2022) (vs 20 or >50)
AI, CS
–
link
2005
Bainbridge
26
Median 2085
Tech
–
link
2006
AI@50
median >50y (2056)
AI conf
–
link
2007
Klein
888
median 2030-2050
Futurism?
–
link and link
2009
AGI-09
21
2020
2040
2075
AGI conf; AI
–
link
2011
FHI Winter Intelligence
35
2028
2050
2150
AGI impacts conf; 44% related technical
41%
link
2011-2012
Kruel interviews
37
2025
2035
2070
AGI, AI
–
link
2012
FHI: AGI-12
72
2022
2040
2065
AGI & AGI impacts conf; AGI, technical work
65%
link
2012
FHI:PT-AI
43
2023
2048
2080
Philosophy & theory of AI conf; not technical AI
49%
link
2012-?
Hanson
~10
≤ 10% progress to human level in past 20y
AI
–
link
2013
FHI: TOP100
29
2022
2040
2075
Top AI
29%
link
2013
FHI:EETN
26
2020
205
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