필터 결과
524개정렬
15:42ENStrawberry and Orion: What Was Reported About OpenAI’s Next Models in 2024Matthew Berman · Forward FutureMatthew Berman examines reports about a reasoning project, synthetic data and a future flagship model. Later releases clarify some of the story, while many original claims remain attributed to unnamed sources.
1:11:17ENImran Khan on IPO Pricing, Public Markets and AI Investment게스트 Imran Khan · 진행자 Harry StebbingsThe investor and former banker explains IPO demand, valuation and the lessons he draws from Alibaba and Snap.
1:40:13ENIf I Were Governor, I Would Sign: California's Last Debate Over SB 1047진행자 Nathan LabenzThree guests return to unpack the rewritten frontier-AI bill as OpenAI opposes it, Anthropic calls it workable, and a final vote looms in Sacramento.
0:54ENEric Schmidt on AI Agents and Google's Competitive PositionMatthew Berman, Eric Schmidt · Forward FutureA short 2024 Stanford interview excerpt pairs a forecast about AI agents with a disputed explanation for Google's AI position.
49:41ZH傅盛:大模型是生产力革命,机器人迎来新的窗口게스트 傅盛 · 진행자 张小珺傅盛认为,大模型的核心是逻辑与推理能力,它带来的变化更像蒸汽机,而不是一次普通的互联网迭代。从平民化模型、服务机器人到创业者如何面对低谷,他也回看了自己的判断与心境。
2:04:15ENA parrot cannot paraphrase — and Claude cannot be talked into harm진행자 Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.
1:09:06ENEthan Mollick on AI Adoption, Education and the Uncertain Path to AGI게스트 Ethan Mollick · 진행자 Harry StebbingsThe Wharton professor argues that model progress is only part of the story: organizations, product design and human choices determine how AI changes work and education.
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.
1:32:45ENHe doubled a company with AI — then asked the labs to pause hyperscaling진행자 Nathan LabenzOn the Nick Halaris Show, Cognitive Revolution host Nathan Labenz argues for adopting today's models fast and slowing the race to systems more powerful than we are.
44:08ENMark Zuckerberg on Llama 3.1, Open Models and Meta’s AI StrategyMatthew Berman, Mark Zuckerberg · Forward FutureMatthew Berman reacts to Zuckerberg’s 2024 interview on open-weight AI, safety, platform control and a future of personalized agents.
50:15ENJason Lemkin on Wiz, CrowdStrike and the SaaS Liquidity Crunch게스트 Jason Lemkin · 진행자 Harry StebbingsThe SaaStr founder discusses a reported acquisition approach, a major outage and the economics of legal-tech and venture exits.
51:36ENKlarna's AI Took 700 Customer-Service Jobs. The Secret Was the ManualCEO Sebastian Siemiatkowski tells Sequoia how an OpenAI guinea-pig bet, a dispute-queue Slack message, and documentation written for an amnesiac employee produced $40 million of profit.
0:58ENMeta Releases Llama 3.1 and Its 405B ModelMatthew Berman · Forward FutureMatthew Berman argues that Meta's open-weight Llama 3.1 puts pressure on proprietary AI providers, while its license and deployment costs add important qualifications.
29:12ENGoldman says AI will touch 5% of tasks. Two investors take the report apart진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil use a host-only No Priors episode to dismantle a June 2024 Goldman Sachs note, then map where they would buy, incubate, and hold.
25:56ENA Prompt Cannot Hold a Good Design: Six AI Practitioners at Midyear 2024진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil stitch together clips from Stripe, Figma, Figure, OpenAI's Sora team, Cognition, and Scale into a half-year snapshot of what people actually shipping AI were willing to claim.
1:06:53ENMatt Clifford on AI Scaling, China, Defense and Finding Founders게스트 Matt Clifford · 진행자 Harry StebbingsThe Entrepreneur First co-founder weighs new AI ideas against compute constraints, examines competing scenarios for China, and explains how he evaluates entrepreneurial talent.
1:48:41ENAGI Has Been a Moving Goalpost for Seventy Years진행자 Nathan Labenza16z’s Martin Casado and host Nathan Labenz spend two hours arguing whether today’s models will become general knowledge workers, and whether that debate should drive AI regulation.
1:09:08ENAndrew Bialecki on Klaviyo’s Bootstrapping, Shopify Partnership and IPO게스트 Andrew Bialecki · 진행자 Harry StebbingsThe Klaviyo CEO discusses early product iteration, customer economics, public markets and his hopes for AI.
1:02:17ENMichael Eisenberg on AI Investing, Liquidity and Venture Discipline게스트 Michael Eisenberg · 진행자 Harry StebbingsThe Aleph investor discusses AI business models, national security, venture exits and the risks of mistaking a technology boom for durable returns.
1:45:11ENAI already beats doctors at empathy. Catastrophe is still not the default진행자 Nathan LabenzNathan Labenz tells Sarah Hastings Woodhouse why he is an adoption accelerationist and a hyperscaling pauser, and lists eight reasons he will not join the hardcore doomers.
52:45ENA $150 million model, $5 million six months later: Benchmark’s AI investment math진행자 Nathan LabenzEric Vishria and Quilter’s Sergiy Nesterenko explain why PCB layout is not a Copilot problem, why human board data would be the wrong training set, and which AI companies look like sugar highs.
1:06:04ENAlexandr Wang on AI Data, Model Development and Building Scale게스트 Alexandr Wang · 진행자 Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.
1:25:42ENReid Hoffman on AI Models, Inflection, Governance and Technology’s Social Impact게스트 Reid Hoffman · 진행자 Harry StebbingsHoffman discusses the Microsoft–Inflection arrangement, AI competition and regulation, and his political views in a time-specific 2024 interview.
1:03:12ENAravind Srinivas on Reasoning, Model Economics and Building Perplexity게스트 Aravind Srinivas · 진행자 Harry StebbingsThe Perplexity co-founder discusses AI scaling, foundation-model competition, advertising and the challenges of building a search product.
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