Artificial General Intelligence Forecasting and Scenario Analysis: State of the Field, Methodological Gaps, and Strategic Implications

TL;DR

This report reviews methodologies for forecasting artificial general intelligence, highlights their limitations, and proposes a research agenda, emphasizing the importance of robust, interpretable approaches amid deep uncertainty.

Contribution

It synthesizes diverse forecasting methods, identifies key methodological gaps, and introduces an iterative AI-human collaboration approach for report drafting.

Findings

Existing forecasting methods have significant limitations.

An iterative AI-human approach can aid in report development.

Frameworks for interpreting forecasts under deep uncertainty are proposed.

Abstract

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.