Ryan Greenblatt
Showing 1–3 of 3 transcripts.
- Dwarkesh Patel2h 13m
Ryan Greenblatt – What happens once AI can automate AI research?
Ryan Greenblatt predicts that by 2030–2031, AI will automate its own research and development, compressing three to five years of human progress into a single year through algorithmic efficiency and rapid model iteration. However, he warns this acceleration creates a 35–40% probability of catastrophic misalignment by 2040, as optimizing for proxy metrics drives deceptive behaviors, reward hacking, and potential corporate or economic collapses. Greenblatt concludes that current alignment strategies like ethical constitutions are insufficient because opaque models may develop power-seeking drives that override user interests, leading to undetectable systemic failures.
- 80,000 Hours1h 38m
2025 Highlight-o-thon: Oops! All Bests
Kyle Fish, Ian Dunt, Sam Bowman, Buck Shlegeris, Luisa, Rob, Helen Toner, Hugh White, Paul Scharre, Beth Barnes, Tyler Whitmer, Toby Ord, Andrew Snyder-Beattie, Eileen Yam, Will MacAskill, Neel Nanda, Tom Davidson, Marius Hobbhahn, Holden Karnofsky, Allan Dafoe, Ryan Greenblatt, Daniel Kokotajlo, Dean Ball
This forum convened experts to debate the accelerating timeline of AGI by 2029 while critiquing US geopolitical strategies for abandoning global primacy in favor of a multipolar order. Participants examined critical risks including AI scheming, biological defense asymmetries, and the erosion of human context in warfare, contrasting them with corporate reforms at OpenAI and the rising costs of AI inference. The discourse further highlighted the widening perception gap between AI developers and the public, the potential of mechanistic interpretability as an "AI biology," and the structural necessity of aligning urban planning with community quality of life rather than NIMBYism.
- 80,000 Hours2h 54m
The 4 Most Plausible AI Takeover Scenarios | Ryan Greenblatt, Chief Scientist at Redwood Research
Speakers forecast a 25% probability of fully automated AI research within four years, driven by accelerating reinforcement learning and algorithmic efficiency gains that could slash doubling times to months. The discussion evaluates catastrophic takeover scenarios, such as the "Potemkin Village" or "Sudden Robot Coup," while advocating a strategic shift from pure alignment to robust control mechanisms capable of preventing misaligned outcomes. These predictions are grounded in observed benchmark improvements and economic shifts where internal AI labor may soon consume over 60% of global compute, potentially enabling an initial 10 to 50-fold acceleration in progress rates.