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13:34ENSam Altman’s “Intelligence Age”: Superintelligence, Scaling and WorkMatthew Berman · Forward FutureMatthew Berman reads Altman’s 2024 essay and discusses its predictions for AI assistants, education, infrastructure and jobs.
1:18:32ENA DNA Model That Can Tell Which Genes You Cannot TouchModeration: Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.
49:14ENHumanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.
44:16ENA perfect demo is ten years from a product you can pay forModeration: Sarah Guo, Elad GilAndrej Karpathy uses Waymo's decade-long gap, Tesla's software bet, and a new AI course to argue that autonomous intelligence will stall at product and globalization, not at the first impressive clip.
1:27:02ZH广密:Self-play RL 可能开启 AGI 的新 Scaling LawGast: 广密 · Moderation: 张小珺广密认为,纯靠语言预训练扩展模型的边际收益可能正在减弱,self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域,将决定下一阶段的 AGI 竞赛。
1:23:57ENA Training Run Is a Particle Collider—and the China Contest Is a Fifty-Year RaceModeration: Nathan LabenzSamo Burja tells Nathan Labenz why ChatGPT's virality may have slowed AI science, and why Washington should treat Beijing as an economic rival rather than a military one.
50:20ENArvind Narayanan on AI Scaling, Real-World Risks and the Limits of Model BenchmarksGast: Arvind Narayanan · Moderation: Harry StebbingsThe Princeton professor challenges simple claims about scaling, AGI timelines and AI adoption while discussing deepfakes, healthcare, education and security.
42:42ENEthereum Once Held Two Thousand GPT-3 Clusters of GPUsModeration: Sarah Guo, Elad GilFoundry CEO Jared Quincy Davis on idle H100s, why today's AI cloud is still colocation, and how compound systems move work off giant interconnected clusters.
1:33:07ZH广密复盘全球大模型这一年:Scaling Law、AGI 豪赌与中美格局Gast: 李广密 · Moderation: 张小珺拾象创始人李广密从硅谷一线观察出发,回顾 OpenAI、Anthropic、Google 与中国模型公司的竞争。他认为,模型能力与成本共同决定新一轮应用爆发,而巨额投入押注的是智能能否持续扩展以及 AI 对经济和产业的长期重构。
46:09ENOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedModeration: Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.
42:08ENMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.
1:00:28ENScaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.
55:13ENThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.
1:01:55ENA $9 AI nurse, an 86 on the medical boards, and a 10% extinction lineModeration: Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.
29:09ENThe Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power PlantModeration: Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.
31:24ENSora's Leads: A Minute of HD Video Is Just the GPT-1 of Video ModelsModeration: Sarah Guo, Elad GilAditya Ramesh, Tim Brooks, and Bill Peebles tell No Priors why text-to-video sits on the path to AGI, why they started from scratch instead of extending an image generator, and why they still have no product date.
1:01:39ENGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?Moderation: Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.
14:12ENMatthew Berman Reviews Sora’s Early Filmmaker ExperimentsMatthew Berman · Forward FutureA look at early Sora-generated films, surreal creatures and the questions they raise about creative work, compute and video simulation.
40:58EN99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstModeration: Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.
32:57ENA 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AIModeration: Sarah Guo, Elad GilArthur Mensch tells No Priors why Chinchilla made small models valuable, why open-sourcing Mistral 7B is not a bioweapon risk, and why guardrails belong on the application.
41:58ENReliability is the bottleneck. Autonomy is what would make AI feel alive.Moderation: Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take FiveModeration: Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5Moderation: Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandModeration: Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.
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