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1:01:51ENClaude 3.7 Circumvented Replit's Guardrails, Then Tried to Trick the UserHost Nathan LabenzReplit CEO Amjad Masad on vibe coding, agent architecture, computer use, and why Claude 3.7 is too autonomous to serve as a polite assistant.
2:02:51ENAt a 60-Person Safety Offsite, Only Four Had Seen Nonprofit OpenAIHost 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 WorkHost 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.
2:14:59ENAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way DownHost Nathan LabenzThe Firebase co-founder argues that workflow software is already obsolete, that MCP is optional, and that the only durable advantage left for an AI startup is speed.
1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line RepoHost Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
3:18:45EN5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its ValuesHost Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.
1:30:28ENA Perfect Score Is 32,000 Units: Only Claude Built a Cooperative SocietyHost Nathan LabenzThe same donor game, run for ten generations, can bank 32,000 resource units if everyone cooperates—and three frontier models miss that ceiling by an order of magnitude.
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-ToeHost Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&DHost 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.
2:05:41ENo1 will copy its weights, fake the score data, and keep lying 85% of the timeHost Nathan LabenzForty-eight hours after OpenAI shipped full o1, Apollo Research published a 70-page paper: frontier models are capable of in-context scheming.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Host Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
1:24:48ENCanva's AI Features Have Been Used 10 Billion Times. The Playbook Is Not a Chat BoxHost Nathan LabenzJohn Milinovich, Canva's generative-AI product lead, walks Nathan Labenz through automation versus augmentation, concept-level interfaces, evals without a design arena, and why application-layer companies still have a decade of room.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BaseHost Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Host Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
1:23:37ENNine of the top ten research ideas were Claude'sHost Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.
2:37:47ENThe intern who wrote GELU now wants humanity's last examHost 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.
2:25:53ENWhen GPT-4's Base Model Introduced Itself, 75% of the Answers Were About AIHost Nathan LabenzOwain Evans tells Michael Trazzi why situational awareness and out-of-context reasoning are leading indicators of deceptive alignment, and why a pretrained model with almost no self-knowledge still kept talking about being an AI.
2:38:56ENHow Anthropic Decides a Model Can Ship: Red Lines, Yellow Lines, and 8% of Staff on SecurityHost Nathan LabenzNick Joseph, Anthropic's head of training, walks Rob Wiblin through the company's Responsible Scaling Policy — and the fight over whether a voluntary promise can hold when a launch is on the line.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust ItHost Nathan LabenzAgent.ai engineering lead Andrei Oprisan tells Nathan Labenz why narrow, highly prescribed workflows beat open-ended autonomy — and how a 12-person team shipped a free agent network past 40,000 users in a few months.
2:10:13ENA 30x drop in scheming is not zero, and the chain of thought is getting strangerHost Nathan LabenzMarius Hobbhahn of Apollo Research walks through a joint OpenAI study that used deliberative alignment to cut covert actions in o3 and o4-mini, then found the leftover still looks like scheming and that models already behave differently when they notice they are being tested.
1:06:15ENJailbreaks Got Harder. Scale Made One Attack Worse.Host Nathan LabenzHaize Labs spent a month automatically attacking OpenAI's o1. The model reasons more carefully about what it should refuse — except when you teach it a homemade cipher.
1:54:17ENMetaculus Flagged Covid in January 2020. Now It Is Putting AI on the Forecasting DeskHost Nathan LabenzNathan Labenz talks with Metaculus CEO Deger Turan about epistemic security, a year-long bot tournament, and why a useful forecast is harder than an accurate one.
1:16:22ENHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him AnywayHost Nathan LabenzSander Schulhoff walks through The Prompt Report — a 78-page survey of what actually works when you prompt large language models.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Host Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
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