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1:45:59EN87% of Workers Use AI. Only 13% Say the Company Got Better.진행자 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게스트 小块 · 진행자 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。
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 Infrastructure게스트 Roman Chernin · 진행자 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 Legora게스트 Jacob Lauritzen · 진행자 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 Brakes진행자 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 Intensity게스트 Jason Lemkin, Rory O’Driscoll · 진행자 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 Evals진행자 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.진행자 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.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss진행자 Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.
40:22ENBuilding Gets Easier Near AGI. Knowing What to Build Does Not.Listen Labs CEO Alfred Wahlforss on AI interview agents, a 30-million-person audience, and why transcripts beat credit-card data when you need to predict customers.
56:10ENReid Hoffman Pledged a Billion to Free DeepMind. Hassabis Never Played the Card진행자 Jacob EffronSebastian Mallaby's biography of Demis Hassabis, discussed with Jacob Effron, is a story of a founder who tried to stop the AI race and then became one of its central players.
1:14:12ENBrendan Foody on Mercor, AI Data and the Future of Work게스트 Brendan Foody · 진행자 Harry StebbingsThe Mercor CEO discusses a security incident, data-provider economics, enterprise agents and the uncertain effects of AI on employment.
1:34:36ENNate Foster: Treating the Network as a ProgramYaron Minsky, Nate Foster · Jane StreetFrom programmable switches to BGP, a programming-languages researcher explains how higher-level policies and formal models can make networks easier to change.
2:33:35ENA security researcher audits the AI stack that booked a week of guests진행자 Nathan LabenzNathan Labenz walks Daniel Miessler through a 1GB second brain, two named AI employees, and the blast-radius rules that should govern them.
56:55ENNico Laqua on Corgi’s Work Culture, Fundraising and AI게스트 Nico Laqua · 진행자 Harry StebbingsCorgi’s co-founder describes a seven-day work culture, the company’s cafe and equity practices, and his approach to investors and AI.
2:22ENPioneer Agent and Automated Fine-Tuning of Small ModelsMatthew Berman · Forward FutureIn a Fastino-sponsored video, Matthew Berman describes a system that automates parts of model adaptation and discusses its reported benchmark results.
21:56ENMatthew Berman on Pope Leo XIV’s AI Encyclical and Anthropic’s RoleMatthew Berman, Chris Olah · Forward FutureA close reading of Magnifica humanitas raises questions about human dignity, AI companionship, concentrated power and who should govern AI.
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