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1:35:16ENPerplexity CEO Aravind Srinivas on AI Agents, Compute and CompetitionInvitado: Aravind Srinivas · Presenta: Harry StebbingsSrinivas discusses the future of AI products, infrastructure constraints, export controls and the challenge of building a business around model advances.
1:25:00ENMatan Grinberg on AI Coding, Token Costs and Building FactoryInvitado: Matan Grinberg · Presenta: Harry StebbingsFactory’s CEO discusses enterprise AI budgets, model choice, engineering culture and the uncertain effects of automation on work.
1:44:28ENFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed overPresenta: 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.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.Presenta: Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
1:15:56EN20VC on IPO Expectations, Lean AI Startups and Investor RiskInvitado: Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported listings, AI company economics and the changing cost of building a startup.
1:00ENBuilding a Bitzee-Inspired Virtual Pet With a Raspberry Pi PicoMatthew Berman · Forward FutureMatthew Berman shows a prototype made with his son, a Pico development kit and AI-assisted coding.
51:09ENBuild like the model will eat your harness in a yearLogan Kilpatrick, who leads Google AI Studio and the Gemini API, tells Sequoia's Sonya Huang why agent scaffolding is a 12-month asset, why coding already feels like narrow superintelligence, and why Google is optimizing for outcomes instead of eyeballs.
1:45:59EN87% of Workers Use AI. Only 13% Say the Company Got Better.Presenta: Nathan LabenzGlean’s Work AI Index names the missing hours: botsitting, botshitting, and a hamster wheel of polished nonsense.
1:21ENHiggsfield Supercomputer: An Espresso Video PromptMatthew Berman · Forward FutureMatthew Berman demonstrates a workflow that uses a reference video and a prompt to create an advertisement.
41:20ENA $4 Million Cabinet and a Trillion-Dollar Year: Jensen Huang Turns the AI Factory Into a Manufacturing StoryOn Sequoia's Training Data stage, NVIDIA's CEO walks a sixty-country audience from ChatGPT's parlor tricks to agentic work, a five-layer industrial cake, and why radiology hiring went up.
10:08ZH小块讲路怒冲突:罗永浩与嘉宾如何把退让讲成喜剧Invitado: 小块 · Presenta: 罗永浩一辆逆行的车、一口痰和一套“幸福者退让原则”,引出一场关于理智与自我安慰的现场抬杠。
1:48:47ENClaude Fable 5 Launch: Live Coding Tests, Costs and SafeguardsMatthew Berman · Forward FutureMatthew Berman reviews a week of early use, troubleshoots the launch-day rollout, and tests browser apps and open mathematical problems live.
12:59ENCoding-Agent Loops: Triggers, Goals and the Cost of RepetitionMatthew Berman, Boris Cherny · Forward FutureMatthew Berman explains event-triggered and scheduled agent workflows, shows examples in Cursor and Claude Code, and considers their limits.
28:16ENClaude Fable 5 and Mythos 5: Matthew Berman Reviews Long-Running Coding WorkMatthew Berman · Forward FutureAn early Fable 5 user examines Anthropic’s benchmarks, safety controls, pricing and coding workflows, then demonstrates a Rubik’s Cube and a fluid simulation.
0:16ENA Skit About Trying to Keep Up With AI NewsMatthew Berman · Forward FutureMatthew Berman jokes about the feeling of catching up just as another coding-agent discussion appears.
1:14:30ENRoman Chernin on AI Compute Demand, Inference and InfrastructureInvitado: Roman Chernin · Presenta: Harry StebbingsNebius’s co-founder discusses customer demand, GPU economics, managed inference and the risks of a concentrated market.
57:42ENJacob Lauritzen on AI Engineering, Legal Work and Building LegoraInvitado: Jacob Lauritzen · Presenta: Harry StebbingsLegora’s CTO discusses code review, token budgets, enterprise adoption and how legal workflows might change.
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.
44:52ENAnthropic’s Recursive Self-Improvement Report: Evidence, Limits and a Debate Over Slowing AIMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s 2026 claims about AI-assisted development, research benchmarks and possible paths to autonomous self-improvement.
1:39:00EN20VC on AI Financing, Token Budgets and the Cost of Startup IntensityInvitado: Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported listings, software valuations, AI spending and demanding work cultures.
1:31:56ENWhen AI Builds Itself: Anthropic’s Recursive Improvement Essay and Its LimitsMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s internal coding and research claims, questions its proposed slowdown, and considers what autonomous AI development could mean.
42:27ENEvery Company Can Operate at the Frontier — If It Owns the EvalsPresenta: Sarah Guo, Elad GilAt Microsoft Build, Satya Nadella tells No Priors and Latent Space why AI is an ecosystem game, why private evals beat public benchmarks, and why the Azure networking team now asks for tokens instead of headcount.
0:47ENCognition's AI Productivity Guarantee for DevinMatthew Berman · Forward FutureMatthew Berman discusses a 2026 enterprise offer that compares estimated engineering value with the cost of using Devin.
2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Presenta: Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
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