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1:08:02ENAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.
2:00:51ENResample Every Token and You Can Watch the Model DecideAnime : Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:30:54ENHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2Anime : Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
2:13:27ENHe Wrote a Book to Stop the AI Race — Then Almost Got Addicted to ClaudeAnime : Nathan LabenzJournalist Garrison Lovely takes the AGI labs at their word, rejects the bubble story, and argues for freezing the frontier until the public actually consents.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconAnime : Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
2:10:42ENThe Exam Papers Nobody AuditsAnime : Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
2:13:09ENOne Chain of Thought, 100 Million Tokens: RL Is a Hell of a DrugAnime : Nathan LabenzApollo Research's Bronson Schoen on reward-seeking models, invented slang, and why chain-of-thought monitoring will not save us.
53:44ENThe Biggest Bug in Today’s Models Is That the Weights Never ChangeTuring Award winner Rich Sutton and Khurram Javed founded Oak Lab to build agents that keep learning after they ship — and they call synthetic data a big mistake.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI SafetyAnime : Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
49:12ENThe People Writing the Transformer’s Eulogy Want a Lab That Can Take Vacation Without Themo1’s reasoning lead and a Gemini pre-training lead argue the architecture, not more scale, is now the bottleneck — and that replacing it starts with automating kernel generation.
50:57ENA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberAnime : Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.
1:57:46EN30% Consciousness, 47% Bee, 80% Turnover: Putting Numbers on What AI Is BecomingAnime : Nathan LabenzA Cognitive Revolution weekly cut that starts inside the model with Cameron Berg, then zooms out through gradual disempowerment, Europe’s bind, and the companies rebuilding around agents.
36:19ENThe Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score ModelsAnime : Sarah Guo, Elad GilOpenAI researcher Noam Brown argues that static leaderboards hide how long a model is allowed to think — and that safety evals, research taste, and the release cycle have not caught up.
1:44:28ENFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed overAnime : Nathan LabenzNathan Labenz’s AI in the AM highlights reel from June 2026 covers Anthropic’s new frontier model, the founding of Sequent, and a week that settled one argument: AI is not slowing down.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the BrakesAnime : Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissAnime : Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.
1:41:58ENA thousand people on guard, a million in the sprintAnime : Nathan Labenz80,000 Hours founder Ben Todd on how to spend a career in the years when your impact on AI will actually peak.
1:21:56ENYann LeCun Left Meta Because LLMs Will Not Get You to IntelligenceAnime : Jacob EffronThe Turing Award winner tells Jacob Effron why he spun AMI Labs out of Meta, why JEPA-style world models beat pixel generators, and why he thinks Hinton changed his mind in 2023.
3:36:29ENClaude Rated Its Own Welfare 4.49 — the First Time Any Version Crossed NeutralAnime : Nathan LabenzCameron Berg returns to The Cognitive Revolution to survey six months of introspection, functional-emotion, and model-welfare research, plus a still-unpublished case that learning and feeling may be the same phenomenon.
3:10:49ENCrunch Time: Ajeya Cotra's Playbook for the Months Before SuperintelligenceAnime : Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.
58:46ENTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028Anime : Jacob EffronJakub Pachocki tells Jacob Effron why those timelines still hold, why continual learning is not a detour, and why showing chain of thought in the product would train the monitor away.
1:49:53ENDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnAnime : Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.
1:34:29ENThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompileAnime : Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
2:25:42ENThe experts argued through the coffee break — and never agreed whether AI can build AIAnime : Nathan LabenzOn a marathon Cognitive Revolution live show, a cancer-AI researcher, a former OpenAI board member, and a national-security analyst each hit a different wall in the boom.
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