Over the past five years, expert forecasts for achieving artificial general intelligence (AGI) — defined as systems capable of performing most economically valuable work at or above human level across a wide range of domains — have shifted substantially from mid-century toward the near term (with some estimates in the 2030s or even sooner). Artificial intelligence (AI) systems are increasingly embedded in critical infrastructure, and decisionmakers — from government officials setting national policy, to investors allocating capital, to laboratory leaders planning research agendas — face a difficult situation in navigating uncertainty about both the timing and nature of advanced AI capabilities.
To help researchers, analysts, and decisionmakers orient to the landscape of AGI forecasting and the key sources of disagreement within it, the authors synthesize diverse AGI forecasting methodologies — including expert surveys, prediction markets, compute-centric models, and scenario analysis — to assess their reliability, identify the sources of expert disagreement, and provide a framework for decisionmaking under uncertainty.
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