Thousands of AI Authors on the Future of AI

Katja Grace, Julia Fabienne Sandkühler, Harlan Stewart, Ben Weinstein-Raun, Stephen Thomas, Zach Stein-Perlman, John Salvatier, Jan Brauner, Richard C. Korzekwa · Journal of Artificial Intelligence Research · 6 Oct 2025

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Where this sits in the debate

Will AI destroy more jobs than it creates?
How fast will the impact arrive? · its finding: it depends (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

A survey of 2,778 researchers who published in top AI venues in October 2023 put a 50% chance on machines outperforming humans at every task by 2047, 13 years earlier than the 2022 survey. Full automation of all occupations was not expected to reach 50% probability until 2116, and most respondents expressed substantial uncertainty about long-term impacts.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

What do researchers who publish in top AI venues expect about the pace, nature and impacts of AI progress?

The approach

A survey of 2,778 researchers in October 2023, with steps to minimize and evaluate participation bias, compared with the authors' 2022 survey of 738 respondents.

The findings

Respondents gave a 50% chance of unaided machines outperforming humans in every task by 2047, 13 years earlier than in 2022, but full automation of all occupations only reached 50% by 2116. 68% thought good outcomes from high-level machine intelligence more likely than bad ones.

How to read it: survey. Self-reported data from firms or individuals — good for perceptions and adoption; answers can differ from behavior.

Limitations and counterpoints AI-written

Forecasts are expert opinion with wide stated uncertainty, and answers depended on how questions were framed.

Abstract

In October 2023, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace, nature and impacts of AI progress. Significant steps were taken to minimize and evaluate bias. In evaluations of participation bias, we found that most groups responded at similar rates. The participants estimated that several milestones had at least a 50% chance of being feasible for AI by 2028, including constructing a payment processing site and fine-tuning an LLM. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027 and 50% by 2047—13 years earlier than in our 2022 survey (N = 738). The chance of all occupations becoming fully automatable, however, was not expected to reach 10% until 2037, and 50% until 2116 (compared to 2164 in the 2022 survey. Most respondents expressed substantial uncertainty about long-term impacts: While 68% in 2023 thought good outcomes from high-level machine intelligence AI were more likely than bad ones, 48% of these net optimists gave at least a 5% chance of extremely bad outcomes. Conversely, 59% of net pessimists gave 5% or more to extremely good outcomes. Depending on how we asked, between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that “substantial” or “extreme” concern is warranted about AI increasing misinformation, boosting authoritarian control, worsening inequality, and other scenarios. There was broad agreement that research aimed at minimizing risks from AI systems ought to be more prioritized.

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2025 (13 months ago)
Citations: 82 (OpenAlex)

82 citations recorded by OpenAlex — too few 2025 papers in this collection for a fair comparison.

□ added to this collection in 2026.