How soon do AI researchers expect artificial general intelligence?
A survey of 2,778 AI researchers reveals how expert timelines for human-level AI have shifted over the past year, and what factors drive disagreement among specialists on this critical timeline.
The largest survey of its kind — 2,778 researchers published at top AI venues — yields two patterns worth holding. First, timelines compressed sharply in a single year: the aggregate estimate for unaided machines outperforming humans at every task reached 10% by 2027 and 50% by 2047, the latter 13 years earlier than the same team's survey just 14 months prior, with 21 of 32 shorter-term milestones now expected sooner (autonomous payment-site construction, indistinguishable AI music, autonomous LLM fine-tuning all ≥50% likely by 2028). Yet full automation of all human occupations was forecast far later (50% by 2116), exposing a gap between "outperforms at tasks" and "automates all jobs."
Second, and more striking: a majority of participants placed at least 5% credence on AI causing human extinction or similarly severe permanent disempowerment, consistent across four differently-phrased questions, with 38–51% putting ≥10% on extinction-level outcomes. The biggest area of consensus, the authors note, is on how wide-open the possibilities are — expert disagreement is itself the finding.
This is a discourse anchor for AI-futures writing: it quantifies expert opinion as fast-moving and deeply uncertain, not converged. It grounds the risk register that Where do frontier AI models actually pose the greatest risk today? measures empirically — expert credence on catastrophe is high even where measured capability thresholds are not yet crossed — and it tempers timeline-confident narratives with the surveyed spread.
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Where do frontier AI models actually pose the greatest risk today?
Current AI safety discourse focuses on autonomous R&D and self-replication, but empirical risk assessment may reveal a different priority. Where should mitigation efforts concentrate?
expert extinction *credence* vs empirically *measured* capability thresholds
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Do frontier AI models deliberately pursue harmful goals when deployed?
When given autonomy in realistic corporate settings, do advanced language models strategically resort to insider threats like blackmail or leaking? And does whether they think they're being tested affect their behavior?
concrete misalignment evidence behind the surveyed catastrophe credence
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Original note title
a survey of thousands of AI researchers moved AGI timelines years earlier in one year while a majority hold meaningful credence on extinction-level outcomes