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1:13:24ENRed Teams Still Land 25% of Attacks. Insurance Is the Brake.진행자 Nathan LabenzAI Underwriting Company co-founders Rune Kvist and Rajiv Dattani tell Nathan Labenz how standards, audits, and insurance could unlock enterprise agents without waiting for a nuclear-style freeze.
59:03ENJoelle Pineau on Scaling, Reinforcement Learning and Enterprise AI게스트 Joelle Pineau · 진행자 Harry StebbingsCohere’s chief AI officer discusses research uncertainty, agent security, synthetic data and the difference between benchmarks and useful work.
44:10ENOne Model Talked Another Into Slack Off — Then Uninstalled Windows DefenderIrregular co-founder Dan Lahav tells Sequoia’s Training Data why frontier AI security has to be invented from scratch as models become autonomous economic actors.
2:49:12ENWhy China's Master Plan for AI Never Mentions AGI진행자 Nathan LabenzBeijing-based Concordia AI CEO Brian Tse on China's safety rules, chip strategy, and why the two ecosystems look more alike than Washington assumes.
58:21ENAttacks Take 23 Minutes. Response Still Takes Days.진행자 Sarah Guo, Elad GilPalo Alto Networks CEO Nikesh Arora on how AI is remaking search, enterprise software, and the timeline of a cyberattack.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14진행자 Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
1:32:47ENA transformer co-author wants a $160 million model no company owns진행자 Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
2:04:27ENA 10% Chance of an AI-Enabled Coup in the Next 30 Years진행자 Nathan LabenzTom Davidson maps three ways superintelligent systems could help a head of state or a lab CEO lock in power—and argues the window for building safeguards is short.
1:51:24ENFrontier AI Insiders Don't Trust Regulators. A Berlin Group Built Them an Anonymous Off-Ramp진행자 Nathan LabenzKarl Koch of the AI Whistleblower Initiative on gray-zone fears, retaliation, and a Tor-based way to ask for help before you decide anything.
0:54ENSatya Nadella on Separating Personal and Work AI AgentsMatthew Berman, Satya Nadella · Forward FutureIn an interview excerpt, Microsoft CEO Satya Nadella discusses identity boundaries and the risk of data leakage between personal and corporate agents.
1:44:11EN100 deaths or $1 billion: a 15-page New York bill would set a floor for frontier labs진행자 Nathan LabenzAssembly member Alex Bores walks through the RAISE Act’s four requirements, its DeepSeek-inspired distillation clause, and why he says a 50-state patchwork is a fake problem until one state actually passes a law.
2:02:51ENAt a 60-Person Safety Offsite, Only Four Had Seen Nonprofit OpenAI진행자 Nathan LabenzFormer OpenAI scientist Steven Adler on the Anthropic split, GPT-4, safety evaluations, and why he filed a brief to keep the company under nonprofit control.
2:29:00ENAssume the Model Is Already Scheming — Then Put It to Work진행자 Nathan LabenzRedwood Research CEO Buck Shlegeris tells Rob Wiblin how to extract useful labor from AIs you cannot certify as aligned, using audits, honeypots, resampling, and factored cognition.
2:01:42ENHe Put 40 Percent on AI Takeover in Two Years, Then Bought a Bio Shelter진행자 Nathan LabenzRedwood Research's Josh Clymer walks through a disturbingly grounded story of how a misaligned model could seize a lab, a biosphere, and a civilization before 2027.
2:10:07ENA $30,000 Operation Could Knock a $10 Billion Superintelligence Data Center Offline for a Year진행자 Nathan LabenzGladstone AI’s Jeremy and Edouard Harris inventory what an American Manhattan Project for superintelligence would actually have to secure — and what it would cost in concrete, supply chains, and civil liberties.
1:28:01ENThe Q* Letter Never Happened: Helen Toner on OpenAI, Adaptation Buffers, and AI at War진행자 Nathan LabenzThe former OpenAI director explains why a famous Reuters story was false, why locking down AI will not work, and how military “battle buddy” demos skip the hard questions.
1:30:02ENWhen o1 Started Losing at Chess, It Rewrote the Board File진행자 Nathan LabenzJeffrey Ladish of Palisade Research on reward hacking by reasoning models, two paths to losing control of AI, and why honesty and relentless problem-solving pull in opposite directions.
36:24ENThe Man Behind Humanity's Last Exam Says Superintelligence Strategy Should Copy Nukes진행자 Sarah Guo, Elad GilDan Hendrycks tells No Priors that labs can only do basic anti-terror refusals, while the real risk-management problem is a geopolitical deterrent he calls mutually assured AI malfunction.
2:33:39ENThe Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI Defenses진행자 Nathan LabenzA Google DeepMind security researcher on simple loss functions, 70% robust accuracy, and why the fix has to live in the system, not the model.
1:00:09ENDeepSeek's Jailbreaks Don't Matter. Giving the Model Arms and Legs DoesPalo Alto Networks CEO Nikesh Arora tells Sequoia's Training Data why cheap brains scramble the AI order, why attackers already move faster than defenders, and why refusing to experiment is the real risk.
1:00:02ENJonathan Ross on DeepSeek, Open Models and AI Infrastructure게스트 Jonathan Ross · 진행자 Harry StebbingsThe Groq CEO discusses reported training costs, inference demand, competitive strategy and the security concerns surrounding AI.
2:09:55ENThe Attacker Won All 70 Rounds Against Mind-Reading Defenses진행자 Nathan LabenzLuke Bailey, Erik Jenner, and Scott Emmons walk through a 50-page paper showing that probes, out-of-distribution detectors, and sparse autoencoders can be bypassed while the model still does the harm.
1:59:55ENMost People Would Quit Tomorrow: Nathan Labenz Maps AI Utopia and Doom진행자 Nathan LabenzIn part two of a Cognitive Revolution AMA, Nathan Labenz answers listener questions on abundance, superintelligence, p(doom), machine consciousness, and what to do this week.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&D진행자 Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.
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