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1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had publishedModeration: Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.
2:14:59ENAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way DownModeration: 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 RepoModeration: Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
1:30:28ENA Perfect Score Is 32,000 Units: Only Claude Built a Cooperative SocietyModeration: 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.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&DModeration: 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.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Moderation: 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.
1:27:33ENA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a DayModeration: Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually ThinkModeration: Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust ItModeration: 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.
1:16:22ENHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him AnywayModeration: 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.Moderation: 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.
4:56ENIf the Mind Is a Tool, AI Looks Less Like a ThreatModeration: Nathan LabenzYohei Nakajima and Nathan Labenz pause a TED AI talk to connect agent-building habits with East–West ideas of identity.
1:45:21EN600,000 Attack Prompts, and Nearly 40% Still Pwned GPT-4Moderation: Nathan LabenzSander Schulhoff walks Nathan Labenz through the prompting habits that still work, the role-play myth that does not, and the global HackAPrompt contest that mapped how language models get hijacked.
1:43:07ENAn Agent Posted Five Tweets — and 5,000 People Didn't NoticeModeration: Nathan LabenzMultiOn founder Div Garg walks Cognitive Revolution through what web agents can actually do in early 2024, what they cost, and why guardrails have to sit outside the model.
1:50:57ENTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry ExperimentModeration: Nathan LabenzGabe Gomes and his Carnegie Mellon students built Coscientist, a language-model agent that plans, codes, and executes real experiments on a remote cloud lab.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Moderation: Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
1:14:48ENDevDay Probably Created Thousands of Startups, Says OpenAI's Logan KilpatrickModeration: Nathan LabenzThe day after OpenAI's first developer conference, Dev Relations lead Logan Kilpatrick tells Nathan Labenz why thin wrappers were never companies, why agents were deliberately not shipped fully autonomous, and what $2 million custom models are for.
1:25:11EN105 lines of code, a million impressions, and a VC who refused to found the companyModeration: Nathan LabenzBabyAGI creator Yohei Nakajima tells Nathan Labenz how a weekend prototype set off the agent wave — and why he went back to writing checks.
1:32:41ENThere are maybe 5,000 people who can train frontier models. The rest need AI engineersModeration: Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.
55:13ENTwo Days to a Prototype: How a16z Turned a Stanford Paper Into AI TownModeration: Nathan LabenzA companion named Alice, a weekend Phaser hack, and Martin Casado's argument that models will never replace formal languages.
1:39:53ENUnder 100 People, 24 Million Users: Replit's Bet on Artificial Developer IntelligenceModeration: Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.
59:41ENHumans Are Single-Threaded. MULTI·ON Wants 100 Agents Running AnywayModeration: Nathan LabenzStanford PhD dropout Div Garg put a personal agent in Chrome so it can reuse your logins, ask for help when it stalls, and stop before it hits Buy.
58:26ENSeventy Percent of the Inbox Is a Pitch: Matt Welsh on the First Wave of AI AgentsModeration: Nathan LabenzThe Fixie.ai founder maps copilot versus delegation, the missing appendages into our systems, and why agents will need guardrails, memory hygiene, and probably an earpiece.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.Moderation: Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.
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