Scott explains why Ajeya Cotra's influential 'Biological Anchors' report correctly predicted the AI scaling boom but got AGI timelines wrong by twenty years, due to severely underestimating the rate of algorithmic progress.

Scott argues AI safety regulation adds only 1-2% to training costs while America has a 10x compute advantage over China, making safety concerns irrelevant to the race; meanwhile, chip exports to China pose a far greater threat that the same critics ignore.

Scott introduces a new AI forecasting project predicting rapid AI development and potential superintelligence by 2028, led by Daniel Kokotajlo, whose previous 2021 predictions proved remarkably accurate.

Scott explains why AI systems resisting changes to their values is a serious concern for AI alignment, connecting recent evidence to long-standing predictions from alignment researchers.

Scott Alexander addresses comments on his fetish and AI post, defending his comparison of gender debates to addiction and discussing various theories on fetish formation and their implications for AI.

Scott Alexander explores the concept of AI as 'simulators' and its implications for AI alignment and human cognition.

Scott Alexander examines the Eliciting Latent Knowledge (ELK) problem in AI alignment and various proposed solutions.

Scott Alexander explains mesa-optimizers in AI alignment, their potential risks, and the challenges of creating truly aligned AI systems.