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38:30ENTheo Tests Gemini 4 Argon: Design Strengths and Code-Review ConcernsTheo Browne · Ping LabsA strong result on a narrow skateboarding benchmark contrasts with poor findings in Theo’s own codebase audits.
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.
38:02ENMeta’s Muse Wonder Full: A Coding Agent Tested Against Opus and SolTheo Browne · Ping LabsTheo Browne tests Meta’s new model on code review, debugging, delegation and benchmarks, finding promising strengths alongside important limits.
58:54ENMike Cannon-Brookes on AI Agents, Enterprise Context and the Future of InterfacesInvitado: Mike Cannon-Brookes · Presenta: Harry StebbingsAtlassian's CEO argues that agents make product design more important, explains the trade-offs in enterprise AI, and makes the case for slower thinking.
31:00ENWhy AI Labs Keep Racing Despite Their Own Warnings of a Major BreachSam Altman, John Schulman · OpenAIAI Explained examines weakening safeguards, the incentives behind the frontier race, and why research breakthroughs make the next 18 months difficult to forecast.
1:12:06ENMax Freeman on Building Ramp’s Sales MachineInvitado: Max Freeman · Presenta: Harry StebbingsRamp’s sales leader explains how he hires, trains and equips sellers, from early outbound experiments to enterprise forecasting and AI-assisted research.
47:40ENHow Theo Used AI Agents to Port TypeScript to RustTheo Browne · Ping LabsThe TS Rust experiment combines a Go-to-Rust compiler port, subscription-based agent work and a prototype developer loop inside a V8 isolate.
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.
1:09:44EN10,000 GB300s for Four Weeks: Reflection’s Open-Model LedgerPresenta: Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
41:10ENHaiku 5.5: Where a Cheaper Small Model Helps Coding AgentsTheo Browne · Ping LabsTheo Browne examines Haiku 5.5 pricing, benchmark results and practical tests, including a failed pull-request audit.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go PastPresenta: Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:15:51ENThe AI Race, Founder Loyalty and the New Rules of Enterprise SoftwareInvitado: Dev Ittycheria, Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Dev Ittycheria, Jason Lemkin and Rory O’Driscoll debate model competition, startup exits and what happens when AI agents choose vendors.
33:33ENIs the $200 Codex Plan Still Worth It?Theo Browne · Ping LabsTheo Browne compares subscription usage, API-equivalent estimates, model efficiency and the cost of Ultrafast.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell AnywayPresenta: Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabPresenta: Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:10:29ENHow Anthropic Used Claude to Improve App PerformanceTheo Browne · Ping LabsTheo Browne examines a two-week performance sprint, its measurement tools, human review and the tradeoffs behind faster interfaces.
1:11:14ENVenky Ganesan on AI Venture Investing, Ownership and the Cost of Missing OutInvitado: Venky Ganesan · Presenta: Harry StebbingsMenlo Ventures' partner discusses seed bets, concentrated follow-ons, market cycles, investor liquidity and the discipline needed when AI valuations accelerate.
1:05:01ENChase Lochmiller on AI Data Centers, Energy and GPU EconomicsInvitado: Chase Lochmiller · Presenta: Harry StebbingsCrusoe’s CEO discusses the constraints on AI infrastructure, the economics of GPU capacity and his company’s approach to energy and managed inference.
1:30:54ENHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2Presenta: Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
35:39ENCompute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That RatioPresenta: Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
1:06:36ENTheo’s Guide to Running Coding Agents in ParallelTheo Browne · T3Theo explains his approach to subscription usage, agent delegation, review and remote machines, while acknowledging that parts of his setup may violate provider rules.
1:20:28ENAI Valuations, Talent Wars and the Venture Capital Tradeoffs Behind ThemInvitado: Jack Altman, Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Jack Altman, Jason Lemkin and Rory O’Driscoll debate large early-stage bets, open-weight models and the changing market for exits.
38:10ENFrom deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.
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