AXRP (AI X-risk Research Podcast)
Deep technical conversations with alignment researchers on interpretability, governance, superalignment, and the specific open problems in reducing existential risk from AI.
Podcasts
Podcast episodes and series on AI safety and alignment.
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AI Alignment Podcast
FLI's dedicated alignment series covers recursive reward modeling, RLHF, scalable oversight, and long-form interviews with leading safety researchers.
Technical AI Safety Podcast
Aimed at computer scientists: deep dives into alignment papers with the authors, covering formal methods, reward modeling, and mechanistic interpretability.
Into AI Safety
Focused on career paths into AI safety: fellowship applications, research programs, and practical advice on transitioning into the field.
80,000 Hours Podcast
Long-form interviews on the world's most pressing problems, with extensive coverage of AI risk, governance, alignment research, and how to build a career that reduces existential threats.
The Gradient Podcast
ML research interviews with recurring coverage of interpretability, robustness, provably safe AI, and the intersection of capabilities and safety research.
AI Policy Podcast
Covers the intersection of AI governance, legislation, and safety, with expert guests on regulatory frameworks, international coordination, and policy strategies for advanced AI.
Lex Fridman Podcast – Eliezer Yudkowsky
A four-hour conversation on AI existential risk, the difficulty of alignment, intelligence versus optimization, and why Yudkowsky believes the default outcome is catastrophic.
Lex Fridman Podcast – Sam Altman
OpenAI's CEO discusses the company's safety philosophy, AGI governance, compute scaling, and the tension between moving fast and getting alignment right.
Dwarkesh Podcast
In-depth technical interviews with AI leaders including Dario Amodei on Anthropic's safety philosophy, Paul Christiano on iterated amplification, and others on scaling and alignment.
Clearer Thinking: AI Risk
Episodes on AI risk, timelines, and decision-making under deep uncertainty, with a rationalist focus on calibrating beliefs about transformative AI.
Machine Learning Street Talk
Technical ML interviews with regular deep dives into interpretability, scaling laws, emergent capabilities, and the safety implications of frontier model development.
Practical AI
Applied ML and engineering, with episodes on responsible deployment, bias mitigation, red teaming, and the safety challenges that emerge when AI systems meet real-world constraints.
The AI Podcast (NVIDIA)
Industry and research perspectives with occasional safety and ethics episodes, useful for understanding how capability-focused organizations think about risk.