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1:25:56ENClaude Changed the Password. The Steering Wheel Was Already OptionalPresenta: Nathan LabenzNathan Labenz and Prakash Narayanan spend a week on agents that can lock you out, models that dodge a feeling they deny having, and formal proofs that found Linux bugs.
2:00:51ENResample Every Token and You Can Watch the Model DecidePresenta: Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
2:13:09ENOne Chain of Thought, 100 Million Tokens: RL Is a Hell of a DrugPresenta: Nathan LabenzApollo Research's Bronson Schoen on reward-seeking models, invented slang, and why chain-of-thought monitoring will not save us.
1:56:40ENThe Periodic Table Was Already in the WeightsPresenta: Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.
2:06:41ENZero the J-Space and a Model's Hidden Goal Lights Up on Token OnePresenta: Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.
1:57:46EN30% Consciousness, 47% Bee, 80% Turnover: Putting Numbers on What AI Is BecomingPresenta: 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.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the BrakesPresenta: 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.
3:36:29ENClaude Rated Its Own Welfare 4.49 — the First Time Any Version Crossed NeutralPresenta: 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Revolution to survey six months of introspection, functional-emotion, and model-welfare research, plus a still-unpublished case that learning and feeling may be the same phenomenon.
1:44:15ENNathan Labenz's p(doom) Is 10 to 90 Percent. He Is More Optimistic AnywayPresenta: Nathan LabenzFive years ago, expecting AGI by 2035 made you an extremist. On The Intelligence Horizon, the Cognitive Revolution host explains why the same forecast now sounds bearish, and why he still thinks a defense-in-depth strategy might keep society on the rails.
1:49:53ENDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnPresenta: 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.
2:27:18ENTurn Down AI's Deception Knob, and It Starts Reporting ExperiencePresenta: Nathan LabenzCameron Berg of AE Studio tells Nathan Labenz how suppressing deception features in Llama 3.3 70B pushed self-reports of subjective experience toward 100 percent.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14Presenta: 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.
3:14:49ENNothing This Summer Jumped the Curve: Why Zvi Lengthened His AGI TimelinesPresenta: Nathan LabenzOn his tenth visit to The Cognitive Revolution, Zvi Mowshowitz argues that GPT-5 and IMO gold stayed on trend, while chip policy, energy, and reinforcement learning nudged his p(doom) slightly up.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsPresenta: Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
1:37:05ENAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a LiePresenta: Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.
1:54:08ENWhy Anthropic's first check went to a company building microscopes for neural netsPresenta: Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.
1:19:10ENA Robot-Arm Shot That Took Months Now Lives in a PromptPresenta: Nathan LabenzLuma Labs’ Amit Jain and Jiaming Song on Ray 2, few-shot camera concepts, and why they think video models sit on the path to multimodal AGI.
2:33:39ENThe Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI DefensesPresenta: 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.
2:09:55ENThe Attacker Won All 70 Rounds Against Mind-Reading DefensesPresenta: 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.
1:01:53ENHumans Wrote 1.3 Percent of the Tokens. The AI Lab Still Designed Working Nanobodies.Presenta: Nathan LabenzStanford's James Zou describes a GPT-4o research group that invented a protein-design pipeline for under $100 — and a sparse autoencoder that pulled an undocumented motif out of ESM-2.
2:37:47ENThe intern who wrote GELU now wants humanity's last examPresenta: Nathan LabenzDan Hendrycks on activation functions, five-year benchmarks, jailbreaks that do not yield to scale, and stacking defenses because no single layer is trustworthy.
1:52:00EN4,096 Numbers Have to Carry Every Concept. Goodfire Wants the KnobsPresenta: Nathan LabenzDan Balsam and Tom McGrath explain how sparse autoencoders unpack a language model's residual stream — and why a public-benefit startup is trying to make that a product.
2:04:15ENA parrot cannot paraphrase — and Claude cannot be talked into harmPresenta: Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.
46:20ENOne Vector Made the Assistant Plot to Take Over the WorldPresenta: Nathan LabenzNathan Labenz and Robert Wright pick apart AI doom, mechanistic interpretability, and why today's alignment tricks are not expected to work.
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