Leopold Aschenbrenner

This extensive essay series, spanning roughly 165 pages, argues that artificial general intelligence could plausibly arrive by 2027 based on a detailed analysis of three converging trends: continued growth in compute budgets, steady improvements in algorithmic efficiency, and the unlocking of additional capabilities through better scaffolding and fine-tuning of existing models. Aschenbrenner, who was a researcher at OpenAI before his departure, presents what he calls the 'straight-line extrapolation' case, arguing that no fundamental breakthrough is required for current approaches to reach transformative capability levels if current trends continue. The essay examines the national security implications of this timeline, arguing that AGI development represents a geopolitical event on the scale of the Manhattan Project and that the United States government is dangerously unprepared for its arrival. He also warns about the security vulnerabilities of leading AI labs, the risks of an uncontrolled intelligence explosion, and the inadequacy of current alignment techniques for systems that may rapidly surpass human intelligence. The series generated significant attention within both the AI safety community and national security circles, and its detailed technical arguments about scaling trajectories have become reference points in debates about the pace of AI progress.

This academic paper develops a formal economic model examining the relationship between technological progress, economic growth, and the probability of existential catastrophe, arguing that existential risk likely follows an inverted U-shape over the course of economic development. The model suggests that as civilizations develop increasingly powerful technologies, the risk of self-destruction initially rises because dangerous capabilities outpace the wisdom and institutions needed to manage them, but eventually falls if the civilization successfully navigates this dangerous period and develops adequate safeguards. This dynamic creates what the author terms a 'time of perils,' a historically unique window during which humanity is powerful enough to destroy itself but has not yet built robust enough protections against catastrophe. Aschenbrenner applies this framework to artificial intelligence, arguing that the development of transformative AI may represent the peak of this danger curve, when the stakes of misalignment or misuse are highest and the window for establishing effective governance is narrowest. The paper bridges the gap between economic growth theory and existential risk scholarship, providing a formal foundation for the intuition that the current era of rapid technological progress may be unusually consequential for humanity's long-term survival.