Daniel Kokotajlo

This report presents a detailed scenario projecting how AI systems could evolve from their current capabilities to artificial superintelligence by the end of the decade, constructed by a team of forecasters with deep expertise in AI capabilities and risk assessment. The scenario traces a year-by-year progression through increasingly capable AI agents, automated AI research, and recursive self-improvement, grounding each step in specific technical milestones and the strategic decisions that labs and governments might make along the way. The authors argue that the combination of continued scaling, algorithmic improvements, and the deployment of AI systems as autonomous researchers could compress what might seem like decades of progress into just a few years. The report is notable for its specificity: rather than offering vague warnings about distant risks, it provides concrete predictions about capability thresholds, economic impacts, and geopolitical dynamics that can be checked against reality as events unfold. Lead author Daniel Kokotajlo departed OpenAI over concerns that the company was not taking safety seriously enough, lending personal credibility to the urgency conveyed in the forecast.

Written in 2021 as a speculative exercise on the forecasting-oriented platform LessWrong, this essay sketches a year-by-year future history from 2022 through 2026 predicting that AI capabilities would advance far more rapidly than mainstream expectations suggested. Kokotajlo forecast that language models would become significantly more capable each year, that AI would begin automating substantial portions of knowledge work, and that the geopolitical implications of these advances would become increasingly acute. What makes this piece remarkable in retrospect is how many of its predictions proved directionally accurate: the essay anticipated the emergence of highly capable chatbots, the acceleration of AI investment, growing public awareness of AI risks, and intensifying competition between major AI laboratories. The piece exemplifies a tradition of quantitative forecasting in the AI safety community that emphasizes making specific, falsifiable predictions rather than offering vague hand-wringing about the future. Kokotajlo's track record as a forecaster contributed to his credibility when he later raised concerns about safety practices at OpenAI and ultimately resigned, forfeiting significant equity to speak publicly about his worries.