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Ex-DeepMind researcher Misha Laskin on building superintelligence, coding as a shortcut, reinforcement learning, launching Reflection AI's first product, Asimov, and competing with the big AI labs 00:00 - Intro 01:42 - Reflection AI: Company Origins and Mission 04:14 - Making Superintelligence Concrete 06:04 - ASI vs. AGI: Why the...

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Matt Turck

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11,971 görüntüleme • 1 yıl önce •via X (Twitter)

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2025 = the year of AI Agents x DAOS x Onchain Capital Allocation NEW greenpill.network podcast: Today, I'm joined by Shaw jin from ai16zdao to talk about AI Agents. How we use them to allocate capital in DAOs? To route information? To accelerate movements? To solve coordination failure? Timestamps for ez navigation 🫡 00:00 - Intro & focusing on DAOs and the intersection of AI agents. 01:00 - AI Agents and Their Impact 02:03 - The Evolution of the Internet 07:52 - Defining AI Agents 09:40 - Reducing Friction in Information Sharing 11:56 - Building a Movement, Not a Cult 14:15 - The Cathedral and the Bazaar Metaphor 16:04 - Using AI for Coordination 17:49 - Reducing Information Siloing 18:39 - Common Tools Across Organizations 19:31 - Open Sourcing Solutions 20:07 - Capital Allocation Challenges 20:55 - AI and Capital Allocation Intersection 21:50 - Human Oversight in Funding 22:48 - Continuous Retroactive Funding 24:42 - Creating a Supportive Culture 25:06 - Feedback Loops for Builders 26:09 - Reducing Overhead in Funding 26:36 - Acknowledging Non-Coding Contributions 27:58 - Chat Summarization Tool 28:34 - Profiles and Social Capital 29:00 - Marketplace of AI Agents 29:46 - Layered AI Systems 32:42 -Human Oversight in AI 34:39 - Feedback Loops in Development 37:01- Documentation and Community Engagement 38:12 - Self-Documenting Code 40:20 - Accelerated Idea Generation 45:03 - Circle of AI Champions 46:32 - Infinite Backrooms Concept 47:48 - Personal Infinite Backroom Ideas 49:23 - Concerns About Backrooms and Information Hiding 51:23 - Infinite Backrooms Concept 53:35 - Implementers and Skill Sharing 54:40 - Vision for the Future 56:39 - Concerns About UBI Implementation 57:56 - Community Income as a Solution 58:14 - A Hope for Self-Sufficiency and AI 59:36 - Outro

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.Jerry Tworek helped drive o1, o3, and Codex at OpenAI where he was VP of Research from 2019 to 2025. Then he left to pursue “types of research that are hard to do at OpenAI.” This week on Unsupervised Learning, I sat down with Jerry to discuss where AI research is headed and what he learned from seven years at the forefront of the field. - Why he left OpenAI after helping create some of its biggest breakthroughs - Why Jerry updated his AGI timeline after building reasoning models - The real limits of scaling reinforcement learning - Why Anthropic has done so well in coding - Inside OpenAI's pivotal decisions - What makes great AI researchers Timestamps: 0:00 Intro 1:26 Scaling Paradigms in AI 3:36 Challenges in Reinforcement Learning 11:48 AGI Timelines 18:36 Converging Labs and Economic Forces 25:05 Jerry's Departure from OpenAI 31:18 Pivotal Decisions in OpenAI's Journey 35:06 Balancing Research and Product Development 38:42 The Future of AI Coding 41:33 Specialization vs. Generalization in AI 48:47 Hiring and Building Research Teams 55:21 Quickfire Listen here: YouTube: Spotify: Apple:

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BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

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Anthropic co-founder Jared Kaplan on scaling and the road to human-level AI. At AI Startup School in San Francisco. 00:17 - From Physics to AI 01:41 - Initial Skepticism and Shift to AI 02:12 - AI Training Phases 02:32 - Pre-Training 03:16 - Reinforcement Learning 04:19 - Scaling Laws in Training 08:19 - Unlocking AI Capabilities 11:27 - Organizational Knowledge and Memory 12:19 - Oversight and Nuanced Tasks 13:38 - Preparing for the Future 15:48 - Claude 4 and Beyond 21:18 - Human-AI Collaboration 29:50 - Scaling Laws and Compute Efficiency 35:26 - Audience Q&A
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Anthropic co-founder Jared Kaplan on scaling and the road to human-level AI. At AI Startup School in San Francisco. 00:17 - From Physics to AI 01:41 - Initial Skepticism and Shift to AI 02:12 - AI Training Phases 02:32 - Pre-Training 03:16 - Reinforcement Learning 04:19 - Scaling Laws in Training 08:19 - Unlocking AI Capabilities 11:27 - Organizational Knowledge and Memory 12:19 - Oversight and Nuanced Tasks 13:38 - Preparing for the Future 15:48 - Claude 4 and Beyond 21:18 - Human-AI Collaboration 29:50 - Scaling Laws and Compute Efficiency 35:26 - Audience Q&A

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Benedict Evans on Why AI Feels Like the Internet in 1997 Benedict Evans joins Erik Torenberg for a conversation on the state of AI, including how coding agents hit product-market fit, why foundation models should be thought of as infrastructure, the value of vertical products, and more. 00:00 Intro 00:44 What's changed since last year 05:53 OpenAI vs Anthropic strategy 10:31 The pricing crunch & platform history 22:48 What comes after coding 38:18 AI & the future of enterprise software 48:43 The CapEx problem 55:07 Will models become commodities? Benedict Evans Erik Torenberg

a16z

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The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
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The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self

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Get the inside story on the development of Gemini's coding capabilities. Listen as the product and research leads for Gemini share their philosophy on what makes a great coding model, the impact of "vibe coding," and the future of programming languages with Logan Kilpatrick, Connie Fan and Danny Tarlow. Timecodes: 0:00 Intro 1:10 Defining Early Coding Goals 6:23 Ingredients of a Great Coding Model 9:28 Adapting to Developer Workflows 11:40 The Rise of Vibe Coding 14:43 Code as a Reasoning Tool 17:20 Code as a Universal Solver 20:47 Evaluating Coding Models 24:30 Leveraging Internal Googler Feedback 26:52 Winning Over AI Skeptics 28:04 Performance Across Programming Languages 33:05 The Future of Programming Languages 36:16 Strategies for Large Codebases 41:06 Hill Climbing New Benchmarks 42:46 Short-Term Improvements 44:42 Model Style and Taste 47:43 2.5 Pro’s Breakthrough 51:06 Early AI Coding Experiences 56:19 Specialist vs. Generalist Models
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Get the inside story on the development of Gemini's coding capabilities. Listen as the product and research leads for Gemini share their philosophy on what makes a great coding model, the impact of "vibe coding," and the future of programming languages with Logan Kilpatrick, Connie Fan and Danny Tarlow. Timecodes: 0:00 Intro 1:10 Defining Early Coding Goals 6:23 Ingredients of a Great Coding Model 9:28 Adapting to Developer Workflows 11:40 The Rise of Vibe Coding 14:43 Code as a Reasoning Tool 17:20 Code as a Universal Solver 20:47 Evaluating Coding Models 24:30 Leveraging Internal Googler Feedback 26:52 Winning Over AI Skeptics 28:04 Performance Across Programming Languages 33:05 The Future of Programming Languages 36:16 Strategies for Large Codebases 41:06 Hill Climbing New Benchmarks 42:46 Short-Term Improvements 44:42 Model Style and Taste 47:43 2.5 Pro’s Breakthrough 51:06 Early AI Coding Experiences 56:19 Specialist vs. Generalist Models

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#카리나 춤 챌린지 모음.zip⋆* 🐈‍⬛ ⁺⑅ 00:00 Weekend 00:19 Sneakers 00:29 Sparkling 00:45 Unforgiven 00:58 퀸카 01:04 Forever 1 01:21 Hard 01:42 New Dance 02:06 Baggy Jeans 02:29 Everyday 02:44 Picture 02:54 Guilty 03:32 샤랄라 03:53 Maniac 04:10 Sweet Venom 04:25 Yeyeye 04:38 Thot Shit 04:48 Good Morning 05:01 Perfect Night 05:30 Chill Kill 05:44 Baddie 06:03 Grand Prix 06:21 Why 06:42 Be There For Me 06:54 Single Ladies 07:11 Rodeo 07:22 첫눈챌린지 07:33 To. X 07:48 Tap
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#카리나 춤 챌린지 모음.zip⋆* 🐈‍⬛ ⁺⑅ 00:00 Weekend 00:19 Sneakers 00:29 Sparkling 00:45 Unforgiven 00:58 퀸카 01:04 Forever 1 01:21 Hard 01:42 New Dance 02:06 Baggy Jeans 02:29 Everyday 02:44 Picture 02:54 Guilty 03:32 샤랄라 03:53 Maniac 04:10 Sweet Venom 04:25 Yeyeye 04:38 Thot Shit 04:48 Good Morning 05:01 Perfect Night 05:30 Chill Kill 05:44 Baddie 06:03 Grand Prix 06:21 Why 06:42 Be There For Me 06:54 Single Ladies 07:11 Rodeo 07:22 첫눈챌린지 07:33 To. X 07:48 Tap

잉꼬

96,150 görüntüleme • 2 yıl önce

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science
27:12

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science

Y Combinator

149,700 görüntüleme • 1 yıl önce

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories
14:21

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

Matt Turck

67,511 görüntüleme • 2 ay önce

How to build a beloved AI product, with Granola CEO Chris Pedregal Granola is the rare AI startup that slipped into a very crowded niche — meeting notes — and still managed to become the product founders (and VCs!) rave about. Plenty of product lessons in this episode. 00:00 - Introduction: The Granola Story 01:41 - Building a "Life-Changing" Product 04:31 - The "Second Brain" Vision 06:28 - Augmentation Philosophy (Engelbart), Tools That Shape Us 09:02 - Late to a Crowded Market: Why it Worked 13:43 - Two Product Founders, Zero ML PhDs 16:01 - London vs. SF: Building Outside the Valley 19:51 - One Year in Stealth: Learning Before Launch 22:40 - "Building For Us" & Finding First Users 25:41 - Key Design Choices: No Meeting Bot, No Stored Audio 29:24 - Simplicity is Hard: Cutting 50% of Features 32:54 - Intuition vs. Data in Making Product Decisions 36:25 - Continuous User Conversations: 4-6 Calls/Week 38:06 - Prioritizing the Future: Build for Tomorrow's Workflows 40:17 - Tech Stack Tour: Model Routing & Evals 42:29 - Context Windows, Costs & Inference Economics 45:03 - Audio Stack: Transcription, Noise Cancellation & Diarization Limits 48:27 - Guardrails & Citations: Building Trust in AI 50:00 - Growth Loops Without Virality Hacks 54:54 - Enterprise Compliance, Data Footprint & Liability Risk 57:07 - Retention & Habit Formation: The "500 Millisecond Window" 58:43 - Competing with OpenAI and Legacy Suites 1:01:27 - The Future: Deep Research Across Meetings & Roadmap 1:04:41 - Granola as Career Coach?
1:08:28

How to build a beloved AI product, with Granola CEO Chris Pedregal Granola is the rare AI startup that slipped into a very crowded niche — meeting notes — and still managed to become the product founders (and VCs!) rave about. Plenty of product lessons in this episode. 00:00 - Introduction: The Granola Story 01:41 - Building a "Life-Changing" Product 04:31 - The "Second Brain" Vision 06:28 - Augmentation Philosophy (Engelbart), Tools That Shape Us 09:02 - Late to a Crowded Market: Why it Worked 13:43 - Two Product Founders, Zero ML PhDs 16:01 - London vs. SF: Building Outside the Valley 19:51 - One Year in Stealth: Learning Before Launch 22:40 - "Building For Us" & Finding First Users 25:41 - Key Design Choices: No Meeting Bot, No Stored Audio 29:24 - Simplicity is Hard: Cutting 50% of Features 32:54 - Intuition vs. Data in Making Product Decisions 36:25 - Continuous User Conversations: 4-6 Calls/Week 38:06 - Prioritizing the Future: Build for Tomorrow's Workflows 40:17 - Tech Stack Tour: Model Routing & Evals 42:29 - Context Windows, Costs & Inference Economics 45:03 - Audio Stack: Transcription, Noise Cancellation & Diarization Limits 48:27 - Guardrails & Citations: Building Trust in AI 50:00 - Growth Loops Without Virality Hacks 54:54 - Enterprise Compliance, Data Footprint & Liability Risk 57:07 - Retention & Habit Formation: The "500 Millisecond Window" 58:43 - Competing with OpenAI and Legacy Suites 1:01:27 - The Future: Deep Research Across Meetings & Roadmap 1:04:41 - Granola as Career Coach?

Matt Turck

32,002 görüntüleme • 10 ay önce

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
39:32

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,448 görüntüleme • 3 ay önce

We cloned an app that does $300M/yr... Using AI (Zero Coding) With (Founding Engineer at ) TIMESTAMPS 00:00 Intro 01:01 Setting Up the Competition 04:54 First Prompt in the 05:07 Generating Assets & Icons 08:07 Implementing Vibe Features 11:56 Oh My God that Song is Fire 22:06 API Integrations (OpenAI ) 27:36 Half Time and Feedback on the App 34:46 App Design and Aesthetic Improvements 35:50 Voice Customization and API Integration 40:21 Creating Onboarding with AI 41:53 Final Adjustments and Testing 56:15 Rileys App Final 59:52 Vishals App Walkthrough
1:05:41

We cloned an app that does $300M/yr... Using AI (Zero Coding) With (Founding Engineer at ) TIMESTAMPS 00:00 Intro 01:01 Setting Up the Competition 04:54 First Prompt in the 05:07 Generating Assets & Icons 08:07 Implementing Vibe Features 11:56 Oh My God that Song is Fire 22:06 API Integrations (OpenAI ) 27:36 Half Time and Feedback on the App 34:46 App Design and Aesthetic Improvements 35:50 Voice Customization and API Integration 40:21 Creating Onboarding with AI 41:53 Final Adjustments and Testing 56:15 Rileys App Final 59:52 Vishals App Walkthrough

Riley Brown

59,063 görüntüleme • 8 ay önce

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
54:56

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,317 görüntüleme • 6 ay önce

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts
1:06:31

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts

sarah guo

2,966,749 görüntüleme • 3 ay önce