필터 결과
89개정렬
32:48ENFourteen People, $200 Million of Compute, and the Reliability Bottleneck for Agents진행자 Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.
1:32:41ENThere are maybe 5,000 people who can train frontier models. The rest need AI engineers진행자 Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.
2:17:30ENGemini Became the Daily Driver. A GPT-5 Worthy of the Name Would Make Agents Work.진행자 Nathan LabenzZvi Mowshowitz returns to Cognitive Revolution to unpack Gemini 1.5, Sora, superalignment, sleeper agents, and the live-player map of frontier labs.
1:39:53ENUnder 100 People, 24 Million Users: Replit's Bet on Artificial Developer Intelligence진행자 Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.
1:45:46ENReplit's $10 AI Programmer Already Pays for Itself. The Ambition Is Bots Hiring Bots진행자 Nathan LabenzTyler Angert, Replit's first product designer, explains Ghostwriter, a virtual developer, and why the company calls itself the perfect substrate for AGI.
41:40ENEvery 10 Milliseconds Cost Copilot 1% of Completions진행자 Sarah Guo, Elad GilGitHub Copilot's chief architect, Alex Graveley, walks through how a leftover code model with a sub-10% test pass rate became a product developers would not quit — and what he is building next at Minion AI.
32:48ENA Hundred Million to Train, a Penny to Infer진행자 Nathan LabenzIn a February 2023 roundtable, two founders argue on mic over whether AGI ends humanity — and who actually captures the value if it does not.
2:51:33ENThe Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's Silence게스트 Wojciech Zaremba · 진행자 Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.
1:25:13ENCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammate진행자 Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.
1:39:51ENAfter the Inflection Point: Simon Willison on Agentic Engineering, Dark Factories and the Coming AI Challenger Disaster진행자 Lenny RachitskyDjango co-creator Simon Willison explains what changed in November 2025, how he works with coding agents, and why prompt injection remains the field's biggest unsolved risk.
1:33:50ENEvals Are the New PRDs: Inside Anthropic's Research Product Team진행자 Lenny RachitskyDianne Penn, Anthropic's head of product for research and Labs, explains how the company found its identity, how her team turns user feedback into evals, and why human judgment still matters.
1:11:06ENReplit's Amjad Masad on Building Your Own Model, Latency, and the Next Billion Software Creators진행자 Jacob EffronThe Replit founder explains why AI should be native to the product, why he trained a small in-house model, and why he thinks software engineering is about to split in two.
1:53:14ENWorth Thousands, Priced at $20: Zvi Mowshowitz on OpenAI's Flurry, Gemini Bets, Deepfakes and a Compute Cap진행자 Nathan LabenzZvi Mowshowitz and Nathan Labenz survey OpenAI's launch spree, AI pricing and bundling, data pollution, deepfakes, the reversal curse, calls to cap training compute, and Amazon's Anthropic deal.
1:04:25ENInside GitHub's Copilot Bet: Agents, Open Source, and Why the Developer Job Endures진행자 Jacob EffronGitHub CEO Thomas explains how Copilot went from a research bet to 1.3 million developers, and what agents, fine-tuning and a multi-model world mean for the next five years of software.
1:32:32ENDevin's Scott Wu on Building an AI Software Engineer and Turning Coders into Architects게스트 Scott Wu · 진행자 Lenny RachitskyThe Cognition co-founder explains how his 15-person team uses fleets of Devins to build Devin, what the product can and can't do, and why he expects more engineers, not fewer.
50:03ENCopilot Is a Copilot, Not a Pilot: Inbal Shani on the Future of AI in Software Development진행자 Lenny RachitskyGitHub's chief product officer explains what AI changes for developers, what it does not replace, and how teams should adopt it.
40:42ENDon't Mistake a Month of Hype for the End of Software진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil separate the SaaS-apocalypse noise from the real signals: record revenue ramps, collapsing token prices, and the question of when a founder should sell.
1:34:57ENFifteen People, Four Products, Zero Handwritten Code: Inside Every's AI-First Company게스트 Dan Shipper · 진행자 Lenny RachitskyEvery CEO Dan Shipper explains how his small team uses Claude Code, an AI operations lead and compounding engineering to do the work of a much larger company.
1:05:00ENFrom Arctic Code Vault to Autocomplete: How GitHub Copilot Went From Moonshot to Product게스트 Ryan J. Salva · 진행자 Lenny RachitskyGitHub VP of product Ryan J. Salva explains how a small research team's experiment became Copilot, and what it took to scale it responsibly.
1:38:45ENWhen Coding Stops Being the Bottleneck: Inside Anthropic's Claude Code and Cowork Teams진행자 Lenny RachitskyFiona Fung, who leads the teams behind Claude Code and Cowork, describes how an engineering org that ships roughly eight times more code manages quality, hiring, metrics and culture.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding Assistant진행자 Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.
1:27:45ENCoding Is Solved, Says the Creator of Claude Code: Boris Cherny on What Comes Next진행자 Lenny RachitskyBoris Cherny, head of Claude Code at Anthropic, on the tool's origin, the principle of latent demand, and how work changes when engineers stop writing code by hand.
1:07:48ENMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI Era진행자 Lenny RachitskyThe creator of DORA and SPACE explains why lines of code and old pipeline metrics fall short with AI, and how to start improving developer experience by listening, surveying and tying results to what leaders care about.
1:41:26ENThe AI Scouting Report, Part 3: What AI Can Already Do, Who Will Win, and Why Nathan Labenz Wants Caution on Scaling진행자 Nathan LabenzNathan Labenz closes his AP-course tour of AI with capabilities, market moats, jobs, law, safety plans and a stance on what to do next.
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