梳理AI通用智能预测方法，揭示短板并提出改进框架。
Artificial General Intelligence Forecasting and Scenario Analysis: State of the Field, Methodological Gaps, and Strategic Implications
- 采用大模型与人类协作撰写，整合多元预测方法。
- 指出现有方法在深度不确定性下可靠性不足。
- 为政策与战略制定提供可解释的分析框架。
本报告综述了预测人工通用智能（AGI）到来的方法现状，评估其可靠性，并分析对战略与政策的影响。我们整合多种预测方法，记录现有方法中的显著局限，并提出构建更稳健预测基础设施的研究议程。报告未支持特定预测或情景，而是提供在深度不确定性条件下解读预测的框架。本报告的主体撰写由大型语言模型（GPT 5.1、Gemini 3 Pro 和 Claude 4.5 Opus）完成，人类研究人员负责方向引导、同行评审、事实核查与修订。
原文摘要 · Abstract (English)
In this report, we review the current state of methodologies to forecast the arrival of artificial general intelligence, assess their reliability, and analyze the implications for strategy and policy. We synthesize diverse forecasting approaches, document significant limitations in existing methods, and propose a research agenda for developing more-robust forecasting infrastructure. The report does not endorse a specific forecast or scenario but rather provides a framework for interpreting forecasts under conditions of deep uncertainty. We experimented with an iterative approach to human and artificial intelligence collaboration for this report. The primary drafting of the text was performed by large language models (GPT 5.1, Gemini 3 Pro, and Claude 4.5 Opus), with human researchers providing direction, peer review, fact-checking, and revision.
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