The Coming Technological Singularity
Vinge coined the Singularity as a near-term horizon beyond which superhuman intelligence makes prediction impossible, framing the urgency that drives alignment timelines today.
AI forecasting & timelines
Scaling laws, takeoff dynamics, emergent abilities, and timeline forecasting for transformative AI.
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Scaling Laws for Neural Language Models
Kaplan et al. quantified predictable performance scaling with compute, data, and parameters, enabling labs to forecast capability jumps and estimate safety lead time.
Training Compute-Optimal Large Language Models (Chinchilla)
Chinchilla reframed scaling laws by showing optimal performance requires balancing model size and training tokens, redirecting how labs plan capability and safety investment.
Emergent Abilities of LLMs
Wei et al. documented capability discontinuities appearing at key scale thresholds, raising concern that dangerous abilities could emerge unpredictably in larger models.
Measuring AI Ability to Complete Long Software Tasks
METR's time-horizon metric showed the length of software tasks AI agents can complete doubling roughly every seven months, giving timeline debates their most concrete and widely cited capability trend line.
The Singularity is Near
Kurzweil presents a maximalist case for merging with machines backed by decades of exponential trend data, shaping how the public and policymakers think about AI timelines.
The Age of Spiritual Machines
Kurzweil's early timeline forecasts shaped modern discourse on AI trajectories and remain a key reference point for evaluating long-horizon predictions.
Superforecasting
Tetlock teaches the cognitive tools needed to predict technological risks with better-than-random accuracy, directly useful for AI timeline and governance forecasting.
Situational Awareness
Aschenbrenner's comprehensive analysis of near-term scaling dynamics, capability trajectories, and the strategic implications of rapid AI progress for labs and states.
Cold Takes
Karnofsky's essays on AI risk, longtermism, and cause prioritization, including the influential Most Important Century series on transformative AI.
AI Impacts
Empirical research on AI timelines, historical technology analogies, and quantitative estimates of AI progress and impact.
AI 2027
The AI Futures Project's month-by-month scenario of superintelligence arriving by 2027, with branching 'race' and 'slowdown' endings — the most-debated concrete picture of how AGI could actually unfold.