Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

The last 5 years have been dominated by LLMs. amit makes a compelling case for visual intelligence dominating the next 5. We discussed the journey from South Park Commons, to Luma, to world models (what does this even mean!?). Enjoy! (00:00) Apple's LiDAR work seeded Luma's founding vision (05:05)...

finn_meeks's profile picture

Finn Meeks

3,188 subscribers

24,544 görüntüleme • 1 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

AI world models & why they matter... in 20 minutes. The holodeck is closer than you think, and the implications for robotics, AR/VR and content creation are staggering. In this video, I'll breakdown everything from Google Genie 3 and NVIDIA Cosmos to startups powering synthetic training data (Parallel Domain, Bifrost etc). Chapters / Time Stamps: 00:00 Introduction 00:39 Dream Worlds at 24 FPS 01:46 Painting the Third Dimension 03:03 The World Model Wars 05:27 Robot Jungle Gyms (Hybrid Approach) 08:23 Synthetic Data Revolution 11:06 Cities That Think 14:07 The Holodeck Approaches 17:49 The Rendering Stack of Reality
20:09

AI world models & why they matter... in 20 minutes. The holodeck is closer than you think, and the implications for robotics, AR/VR and content creation are staggering. In this video, I'll breakdown everything from Google Genie 3 and NVIDIA Cosmos to startups powering synthetic training data (Parallel Domain, Bifrost etc). Chapters / Time Stamps: 00:00 Introduction 00:39 Dream Worlds at 24 FPS 01:46 Painting the Third Dimension 03:03 The World Model Wars 05:27 Robot Jungle Gyms (Hybrid Approach) 08:23 Synthetic Data Revolution 11:06 Cities That Think 14:07 The Holodeck Approaches 17:49 The Rendering Stack of Reality

Bilawal Sidhu

111,582 görüntüleme • 11 ay önce

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks
26:35

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,561 görüntüleme • 1 ay önce

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI
57:23

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

Y Combinator

151,332 görüntüleme • 3 ay önce

.Satya Nadella on: - why he doesn’t believe in AGI but does believe in 10% economic growth - Microsoft’s new topological qubit breakthrough and gaming world models - whether Office commoditizes LLMs or the other way around Links below. Enjoy! Timestamps 0:00:00 - Intro 0:05:48 - AI won't be winner-take-all 0:16:02 - World economy growing by 10% 0:22:23 - Decreasing price of intelligence 0:31:03 - Microsoft's Quantum breakthrough 0:43:35 - Microsoft's gaming world model 0:50:35 - Legal barriers to AI 0:56:30 - Getting AGI safety right 1:05:43 - 34 years at Microsoft 1:11:31 - Does Satya Nadella believe in AGI?
1:16:54

.Satya Nadella on: - why he doesn’t believe in AGI but does believe in 10% economic growth - Microsoft’s new topological qubit breakthrough and gaming world models - whether Office commoditizes LLMs or the other way around Links below. Enjoy! Timestamps 0:00:00 - Intro 0:05:48 - AI won't be winner-take-all 0:16:02 - World economy growing by 10% 0:22:23 - Decreasing price of intelligence 0:31:03 - Microsoft's Quantum breakthrough 0:43:35 - Microsoft's gaming world model 0:50:35 - Legal barriers to AI 0:56:30 - Getting AGI safety right 1:05:43 - 34 years at Microsoft 1:11:31 - Does Satya Nadella believe in AGI?

Dwarkesh Patel

2,263,770 görüntüleme • 1 yıl önce

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?
1:42:41

We don't know what most microbial genes do. Can genomic language models help? there's only one way to find out! this is a 1 hour and 42 minute interview with an MIT professor (the famous Yunha Hwang) chatting about these questions, her work in solving them at Tatta Bio, and more. zoomer captions are back too Links in reply! Timestamps: 00:00:00 - Clips + sponsor roll from the wonderful LatchBio 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years?

owl

44,279 görüntüleme • 7 ay önce

my OpenClaw agent know more than you about the FIFA World Cup - and this video proves exactly why. we're on a journey to build the worlds most intelligent football model ahead of the World Cup and make money from it. 00:00 - Episode Three, 81 Days left. 00:29 - Football Intelligence - The World Cup Skill. 02:23 - Listing the WC Skill on Virtuals Protocol Marketplace. 03:20 - What are we even selling? (Breakdown). 06:27 - It's not all sunshine & rainbows... it broke. 09:07 - We built a prediction skill - yep - for real. 10:47 - Selling the prediction on Virtuals. 12:07 - Ready to go, it's money time... Ready??? Gaffer is live, it's already making money. But right now... it's TIME for episode 3.
12:41

my OpenClaw agent know more than you about the FIFA World Cup - and this video proves exactly why. we're on a journey to build the worlds most intelligent football model ahead of the World Cup and make money from it. 00:00 - Episode Three, 81 Days left. 00:29 - Football Intelligence - The World Cup Skill. 02:23 - Listing the WC Skill on Virtuals Protocol Marketplace. 03:20 - What are we even selling? (Breakdown). 06:27 - It's not all sunshine & rainbows... it broke. 09:07 - We built a prediction skill - yep - for real. 10:47 - Selling the prediction on Virtuals. 12:07 - Ready to go, it's money time... Ready??? Gaffer is live, it's already making money. But right now... it's TIME for episode 3.

jordy

20,723 görüntüleme • 3 ay önce

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
49:29

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

Molly O’Shea

69,971 görüntüleme • 9 ay önce

Everyone knows alon But @pumpdotfun actually has three co-founders We hosted Sapijiju for his first-ever conversation about $PUMP Timestamps 0:00 Intro & Noah's Crypto Journey 7:15 Learning from Failure to Product Market Fit 11:20 Crypto's Lack of Innovation 16:30 Current Creator Models are Broken 18:00 Pump's Content Vision 23:00 Pump as a Product First Operation 25:00 Growing the Pie for Streaming 29:00 Innovating on Content Distribution 34:00 Letting Content Grow Organically 37:40 +$1.5B worth of SOL is locked in Pump Liquidity Pools 40:20 Creating a Global Impact with Pump 42:20 What could Pump do with its Warchest? 47:00 The Pump Buybacks 51:00 Why People use Pump 54:00 The Culture Shift of Crypto 57:00 Traditional models no Longer Work 59:00 The Cycle of Human Nature
1:06:36

Everyone knows alon But @pumpdotfun actually has three co-founders We hosted Sapijiju for his first-ever conversation about $PUMP Timestamps 0:00 Intro & Noah's Crypto Journey 7:15 Learning from Failure to Product Market Fit 11:20 Crypto's Lack of Innovation 16:30 Current Creator Models are Broken 18:00 Pump's Content Vision 23:00 Pump as a Product First Operation 25:00 Growing the Pie for Streaming 29:00 Innovating on Content Distribution 34:00 Letting Content Grow Organically 37:40 +$1.5B worth of SOL is locked in Pump Liquidity Pools 40:20 Creating a Global Impact with Pump 42:20 What could Pump do with its Warchest? 47:00 The Pump Buybacks 51:00 Why People use Pump 54:00 The Culture Shift of Crypto 57:00 Traditional models no Longer Work 59:00 The Cycle of Human Nature

Delphi Digital

257,250 görüntüleme • 10 ay önce

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
1:13:56

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI

Matt Turck

100,864 görüntüleme • 1 ay önce

We are entering the era of Fintech 3.0. Regulatory clarity, growing consumer adoption, and low-cost chains have paved the way for a golden age of building in crypto — and at YC, Base and Coinbase 🛡️ we want to fund builders to seize this moment. Harj Taggar and jesse.base.eth sat down to discuss what kinds of companies they're most excited to see, why this is such an exciting time, and what the future could look like onchain. 00:00 – The Golden Age of Crypto 02:00 – Jesse’s Path to Coinbase & Base 04:00 – From Fintech 1.0 to Fintech 3.0 07:00 – Why Lowering Costs Changes Everything 10:00 – L1 vs. L2, Simply Explained 13:00 – How to Build Your First Crypto App 16:00 – Regulation: Bottleneck or Unlock? 19:00 – Stablecoins as the Killer Use Case 23:00 – Beyond the Dollar: Local Stablecoins 27:00 – Tokenization of Assets 31:00 – New Asset Classes & the Creator Economy 35:00 – What Coinbase Looks for in Founders 39:00 – The Future of Crypto in 10 Years
42:00

We are entering the era of Fintech 3.0. Regulatory clarity, growing consumer adoption, and low-cost chains have paved the way for a golden age of building in crypto — and at YC, Base and Coinbase 🛡️ we want to fund builders to seize this moment. Harj Taggar and jesse.base.eth sat down to discuss what kinds of companies they're most excited to see, why this is such an exciting time, and what the future could look like onchain. 00:00 – The Golden Age of Crypto 02:00 – Jesse’s Path to Coinbase & Base 04:00 – From Fintech 1.0 to Fintech 3.0 07:00 – Why Lowering Costs Changes Everything 10:00 – L1 vs. L2, Simply Explained 13:00 – How to Build Your First Crypto App 16:00 – Regulation: Bottleneck or Unlock? 19:00 – Stablecoins as the Killer Use Case 23:00 – Beyond the Dollar: Local Stablecoins 27:00 – Tokenization of Assets 31:00 – New Asset Classes & the Creator Economy 35:00 – What Coinbase Looks for in Founders 39:00 – The Future of Crypto in 10 Years

Y Combinator

400,091 görüntüleme • 9 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 • 7 ay önce

Why do even our best AI models need tens of thousands of examples to learn skills that a human picks up in a handful of tries? Solving this problem is one of the great open challenges in modern AI. World models, which give AI an internal simulation of its environment, are one of the most promising paths forward. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard discuss the intuition and math behind world models, new research, and current applications in self-driving, robotics, and more. 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 1:04:30 — Does this pass the squint test?
1:14:26

Why do even our best AI models need tens of thousands of examples to learn skills that a human picks up in a handful of tries? Solving this problem is one of the great open challenges in modern AI. World models, which give AI an internal simulation of its environment, are one of the most promising paths forward. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard discuss the intuition and math behind world models, new research, and current applications in self-driving, robotics, and more. 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 1:04:30 — Does this pass the squint test?

Y Combinator

96,186 görüntüleme • 2 gün önce

.Jerry Tworek is an AI legend. He came to OpenAI from the world of finance and spent years contributing to some of the company's most important projects. He played a major role in ushering in the era of reasoning and, well, he just left OpenAI. In this Core Memory pod, Kylie Robison and I bring you the exclusive not-so-formal exit interview with Jerry. He explains his dissatisfaction with the company's current research direction and his fears for the field overall. He goes into spots where OpenAI lost its way and Google and Anthropic thrived. And he talks about what he's chasing next. It's a banger. The Core Memory podcast is on all major platforms and on our YouTube channel. This podcast is sponsored by Brex , the intelligent finance platform built to help companies spend smarter and move faster. And it's brought to you by the VC gurus E1 Ventures 🇺🇸 Timestamps 00:00:00 Intro 00:03:37 Jerry Tworek Joins the Pod 00:05:34 Reflecting on Seven Years at OpenAI 00:09:08 Why Jerry Left: 00:12:28 Beyond Pre-Training 00:16:19 The "Sad" Homogeneity of Current AI Labs 00:23:35 The Mavericks: Carmack, Ilya, and LeCun 00:26:32 Training AI on Video Games 00:32:14 Two Big Bets 00:34:32 Updating AGI Timelines 00:37:16 Q-Star 00:41:10 The Coup 00:43:29 Is the Hype Justified? 00:49:23 The Polish Mafia 00:53:15 Google's Comeback and OpenAI's Fumble 00:59:27 Why Anthropic is Impressive 01:03:01 Jerry's Next Chapter 01:11:56 Is AI Research Star-Driven? 01:14:32 Meditation & Conclusion
1:16:29

.Jerry Tworek is an AI legend. He came to OpenAI from the world of finance and spent years contributing to some of the company's most important projects. He played a major role in ushering in the era of reasoning and, well, he just left OpenAI. In this Core Memory pod, Kylie Robison and I bring you the exclusive not-so-formal exit interview with Jerry. He explains his dissatisfaction with the company's current research direction and his fears for the field overall. He goes into spots where OpenAI lost its way and Google and Anthropic thrived. And he talks about what he's chasing next. It's a banger. The Core Memory podcast is on all major platforms and on our YouTube channel. This podcast is sponsored by Brex , the intelligent finance platform built to help companies spend smarter and move faster. And it's brought to you by the VC gurus E1 Ventures 🇺🇸 Timestamps 00:00:00 Intro 00:03:37 Jerry Tworek Joins the Pod 00:05:34 Reflecting on Seven Years at OpenAI 00:09:08 Why Jerry Left: 00:12:28 Beyond Pre-Training 00:16:19 The "Sad" Homogeneity of Current AI Labs 00:23:35 The Mavericks: Carmack, Ilya, and LeCun 00:26:32 Training AI on Video Games 00:32:14 Two Big Bets 00:34:32 Updating AGI Timelines 00:37:16 Q-Star 00:41:10 The Coup 00:43:29 Is the Hype Justified? 00:49:23 The Polish Mafia 00:53:15 Google's Comeback and OpenAI's Fumble 00:59:27 Why Anthropic is Impressive 01:03:01 Jerry's Next Chapter 01:11:56 Is AI Research Star-Driven? 01:14:32 Meditation & Conclusion

Ashlee Vance

11,701 görüntüleme • 5 ay önce

Substack co-founder and CEO Chris Best (Chris Best) is building the publishing platform for independent creators — one where creators succeed by serving their communities. Millions of people now use the app every week, paying for the work they value and fueling a new economic engine for culture. On the Social Radars, Chris shares how a failed essay became their founding manifesto, why their first writer made $100K on day one, and what it means to stay in founder mode while building a platform that reshapes the future of media. 01:05 – The Essay That Sparked Substack 03:00 – $100K on Day One 05:10 – Early Struggles and Conviction to Continue 08:00 – Why Substack Resonated with Writers 11:00 – From Tool to Network Effect 14:20 – Business Model and Growth Levers 17:40 – Substack’s Place in the Media Landscape 21:00 – Building Notes and Clashing with Elon Musk 24:00 – Lessons in Staying in Founder Mode 26:20 – Long-Term Vision for Substack
27:53

Substack co-founder and CEO Chris Best (Chris Best) is building the publishing platform for independent creators — one where creators succeed by serving their communities. Millions of people now use the app every week, paying for the work they value and fueling a new economic engine for culture. On the Social Radars, Chris shares how a failed essay became their founding manifesto, why their first writer made $100K on day one, and what it means to stay in founder mode while building a platform that reshapes the future of media. 01:05 – The Essay That Sparked Substack 03:00 – $100K on Day One 05:10 – Early Struggles and Conviction to Continue 08:00 – Why Substack Resonated with Writers 11:00 – From Tool to Network Effect 14:20 – Business Model and Growth Levers 17:40 – Substack’s Place in the Media Landscape 21:00 – Building Notes and Clashing with Elon Musk 24:00 – Lessons in Staying in Founder Mode 26:20 – Long-Term Vision for Substack

Y Combinator

24,955 görüntüleme • 10 ay önce

ElevenLabs CEO: Why Voice is the Next AI Interface ElevenLabs is one of the most interesting companies in AI today. In just three years, they’ve gone from two cofounders from Poland to dominating the AI voice industry. ElevenLabs does their own R&D, builds their own foundation models, and ships over 10 different products at lightning speed without compromising on quality. We sat down with ElevenLabs co-founder and CEO Mati Staniszewski to discuss the breadth of their product roadmap, why they aren’t based in SF, how they’re working to overcome opposition in creative industries, and the hardest parts of Mati’s journey from small startup founder to industry titan. 00:00 Intro 02:50 Early research and product work with Piotr 03:35 Shipping quickly with small, high ownership independent teams 04:40 Balancing research and product launches 06:50 A remote-first approach: meeting talent where they are 10:00 US vs Europe work cultures 13:35 The creative industry’s adoption of AI 16:43 Challenges in licensing and 18-month negotiation process 18:05 Hiring in complex domains 20:45 Transitioning from creator-first to enterprise adoption 21:48 Lessons from hiring the first salespeople 23:34 Scaling orchestration, long sales cycles and cultural adjustments 26:22 Customer choice in adopting early features 27:55 Phases of company growth: product, sales, scaling Jennifer Li
31:29

ElevenLabs CEO: Why Voice is the Next AI Interface ElevenLabs is one of the most interesting companies in AI today. In just three years, they’ve gone from two cofounders from Poland to dominating the AI voice industry. ElevenLabs does their own R&D, builds their own foundation models, and ships over 10 different products at lightning speed without compromising on quality. We sat down with ElevenLabs co-founder and CEO Mati Staniszewski to discuss the breadth of their product roadmap, why they aren’t based in SF, how they’re working to overcome opposition in creative industries, and the hardest parts of Mati’s journey from small startup founder to industry titan. 00:00 Intro 02:50 Early research and product work with Piotr 03:35 Shipping quickly with small, high ownership independent teams 04:40 Balancing research and product launches 06:50 A remote-first approach: meeting talent where they are 10:00 US vs Europe work cultures 13:35 The creative industry’s adoption of AI 16:43 Challenges in licensing and 18-month negotiation process 18:05 Hiring in complex domains 20:45 Transitioning from creator-first to enterprise adoption 21:48 Lessons from hiring the first salespeople 23:34 Scaling orchestration, long sales cycles and cultural adjustments 26:22 Customer choice in adopting early features 27:55 Phases of company growth: product, sales, scaling Jennifer Li

a16z

144,000 görüntüleme • 8 ay önce

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question
26:53

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question

The Humanoid Hub

96,465 görüntüleme • 1 yıl önce

It has been a long time since I made a video. I am sharing a deep dive into 2fac, a new 2FA app I built to replace Authy. What makes this project special is its massive scope, running natively across Android, iOS, watchOS, desktop, CLI, browser extensions, and the web. Building this many features across so many platforms was only possible through agentic coding. Huge thanks to Copilot, Claude, OpenCode, and Pi for completely changing how I write software. Here is what is covered in the video. In the pre-AI world, so many features, taking it to such a polished state (ui, design, relialibility of how it works, and quality of code too, you can take a look for yourelf) single handedly, that too working on spare time on the side from a day job, was simply not possible for non 10x, mid engineers like me 00:00:00 Project Overview & Motivation 00:03:31 Android App Demo 00:04:19 iOS & watchOS App Demo 00:05:32 Desktop App Demo 00:06:05 System Tray Integration 00:07:05 CLI Demo 00:08:44 Browser Extension Demo 00:10:00 Web App (PWA) Demo 00:11:40 Secure Unlock & Passkeys 00:16:30 GitHub Repo & Release Plans 00:17:30 CI/CD Pipeline Setup 00:19:00 AI Agentic Coding Workflow 00:23:50 Production Usage Warning 00:26:00 Custom AI Agent Skills 00:28:30 Automated Planning & Roadmaps 00:31:14 UI Testing with Maestro 00:35:12 Creating Custom AI Agents 00:38:34 Codebase Test Coverage 00:41:30 AI-Driven TDD
43:11

It has been a long time since I made a video. I am sharing a deep dive into 2fac, a new 2FA app I built to replace Authy. What makes this project special is its massive scope, running natively across Android, iOS, watchOS, desktop, CLI, browser extensions, and the web. Building this many features across so many platforms was only possible through agentic coding. Huge thanks to Copilot, Claude, OpenCode, and Pi for completely changing how I write software. Here is what is covered in the video. In the pre-AI world, so many features, taking it to such a polished state (ui, design, relialibility of how it works, and quality of code too, you can take a look for yourelf) single handedly, that too working on spare time on the side from a day job, was simply not possible for non 10x, mid engineers like me 00:00:00 Project Overview & Motivation 00:03:31 Android App Demo 00:04:19 iOS & watchOS App Demo 00:05:32 Desktop App Demo 00:06:05 System Tray Integration 00:07:05 CLI Demo 00:08:44 Browser Extension Demo 00:10:00 Web App (PWA) Demo 00:11:40 Secure Unlock & Passkeys 00:16:30 GitHub Repo & Release Plans 00:17:30 CI/CD Pipeline Setup 00:19:00 AI Agentic Coding Workflow 00:23:50 Production Usage Warning 00:26:00 Custom AI Agent Skills 00:28:30 Automated Planning & Roadmaps 00:31:14 UI Testing with Maestro 00:35:12 Creating Custom AI Agents 00:38:34 Codebase Test Coverage 00:41:30 AI-Driven TDD

Arnav Gupta

109,380 görüntüleme • 3 ay önce

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54
53:37

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Dan Shipper 📧

26,900 görüntüleme • 3 ay önce

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing
41:16

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,533 görüntüleme • 24 gün önce

Jensen Huang is a titan and a teacher. He recently sat down with me to explain his vision of the future of technology and humanity. He’s calm, clear, and very funny. We touched on many topics, ranging from the $20T or more AI economy in five layers to changes in labor for the AI age. Here are some of my main takeaways: 1) The world is moving from retrieval to generation 2) Generation offers intelligence customized to the individual 3) Nvidia is making the "generators" of intelligence 4) We've seen this kind of revolution at least three times before with energy (Generators), Telecommunications (vacuum tubes / transistors?), and now intelligence (GPUs) 5) There is a five-layer cake of participation in this many-many trillion dollar revolution: Energy, Chips, Infra, Models, and Applications. 6) There are many ways to participate in this revolution, and everyone has a role 7) We'll be pushed to dream up new problems to solve with this unprecedented intelligence 8) In this new future, it's not just having the answer, it's having the right questions 8) The right questions will drive us toward our individual and collective human purpose 9) We move from the carpenters to the architects I believe this is the realistic future. Thanks to Jensen and the entire NVIDIA team for the conversation and for letting us share! 00:00 Introduction 00:42 From Chatbots to Generative AI 03:35 Agentic AI That Does Work 05:26 Downstream Industry Impact 06:25 Computing Shifts From Retrieval to Generation 11:26 A Planet Cocooned by Intelligence 14:27 Inside the NVIDIA AI Factory 20:48 AI Five Layer Cake 21:58 Beyond Chatbots to Biology 23:54 Tokens and World Models 24:53 Trillions in Applications 27:13 Ditch the AI Doom 31:32 Jobs Tasks vs Purpose 38:40 Closing the Tech Divide
41:20

Jensen Huang is a titan and a teacher. He recently sat down with me to explain his vision of the future of technology and humanity. He’s calm, clear, and very funny. We touched on many topics, ranging from the $20T or more AI economy in five layers to changes in labor for the AI age. Here are some of my main takeaways: 1) The world is moving from retrieval to generation 2) Generation offers intelligence customized to the individual 3) Nvidia is making the "generators" of intelligence 4) We've seen this kind of revolution at least three times before with energy (Generators), Telecommunications (vacuum tubes / transistors?), and now intelligence (GPUs) 5) There is a five-layer cake of participation in this many-many trillion dollar revolution: Energy, Chips, Infra, Models, and Applications. 6) There are many ways to participate in this revolution, and everyone has a role 7) We'll be pushed to dream up new problems to solve with this unprecedented intelligence 8) In this new future, it's not just having the answer, it's having the right questions 8) The right questions will drive us toward our individual and collective human purpose 9) We move from the carpenters to the architects I believe this is the realistic future. Thanks to Jensen and the entire NVIDIA team for the conversation and for letting us share! 00:00 Introduction 00:42 From Chatbots to Generative AI 03:35 Agentic AI That Does Work 05:26 Downstream Industry Impact 06:25 Computing Shifts From Retrieval to Generation 11:26 A Planet Cocooned by Intelligence 14:27 Inside the NVIDIA AI Factory 20:48 AI Five Layer Cake 21:58 Beyond Chatbots to Biology 23:54 Tokens and World Models 24:53 Trillions in Applications 27:13 Ditch the AI Doom 31:32 Jobs Tasks vs Purpose 38:40 Closing the Tech Divide

Konstantine Buhler

183,773 görüntüleme • 1 ay önce