“Bayeswatch: A Retrospective” by lsusr episode artwork

EPISODE · Aug 2, 2026 · 9 MIN

“Bayeswatch: A Retrospective” by lsusr

from LessWrong (30+ Karma)

Last year, in 2025, a team of forecasters published AI 2027, a science fiction story about how and AI future might evolve under an international treaty limiting the development of powerful AI systems with the deliberate purpose of influencing AI policy. Though AI 2027 is the most popular story of this type, it is not the first. The first one was Bayeswatch, which I published in 2021. To understand Bayeswatch, it is first necessary to understand what the world looked like at the time I wrote it. 2021 was after the release of GPT-3, before the release of ChatGPT, and well before the release of Claude code. AI alignment discussion at the time was mostly theoretical. After that came technical work. Policy work was a distant third, and theoretical too. The core conceit of Bayeswatch is that solving the alignment problem requires international coordination of major governments to suppress the creation of the most powerful AI systems. I felt that, in 2021, we weren't yet close enough to the singularity that the benefits of regulation outweighed the costs. I wanted to draw attention to the costs of an AI slowdown. Since AI alignment discussion at the time skewed theoretical [...] ---Outline:(03:30) Bayeswatch 1: Jewish Space Laser(04:05) Bayeswatch 2: Puppy Muffins(04:28) Bayeswatch 3: A Study in Scarlet(04:58) Bayeswatch 4: Mousetrap(05:37) Bayeswatch 5: Hivemind(05:59) Bayeswatch 6: Mechwarrior(06:21) Bayeswatch 7: Wildfire(06:52) Bayeswatch 8: Antimatter(07:17) Bayeswatch 9: Zombies(07:46) Bayeswatch 10: Spyware(08:03) Bayeswatch 11: Parabellum(08:18) Bayeswatch 12: The Singularity War(08:49) Bayeswatch 13: Spaceship(09:12) Final Thoughts --- First published: August 1st, 2026 Source: https://www.lesswrong.com/posts/EaNkLdsuDQMFCW7ow/bayeswatch-a-retrospective --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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