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Sam Altman on OpenAI’s next model and the AI backlash I recently sat down with Sam for a two-part conversation. We covered a lot of ground: the need to slow down frontier research, alignment concerns, OpenAI's next big model, Astra, competition with Anthropic, his thoughts on an IPO, reaching...

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Alex Heath

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198,067 просмотров • 8 часов назад •via X (Twitter)

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My conversation with Sam Altman (Sam Altman), CEO of OpenAI. We discuss: - Kimi, distillation, and open source - OpenAI's compute bets - The Hugging Face incident - What happens after AGI - Raising kids in an age of abundant intelligence - And much more Enjoy! TIMESTAMPS 0:00 Intro 4:10 The Race for Compute 14:24 A Sci-Fi Cyber Incident 16:14 The Promise and Risks of AGI 23:27 How AI Will Change Jobs 29:38 Sam’s Vision for a Personal AI 35:02 Robotics, ChatGPT, and What Comes Next 44:39 The Weight of Leading OpenAI 51:36 OpenAI’s Biggest Lessons
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Nathan Lands

28,872 просмотров • 4 месяцев назад

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Raoul Pal

326,827 просмотров • 1 год назад

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏
45:47

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏

Michael Mignano

25,155 просмотров • 1 год назад

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future
2:47:07

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future

Lex Fridman

1,022,504 просмотров • 2 лет назад

Every conversation I have with Dylan Patel, I'm really just trying to understand the supply and demand of tokens. This is a unique episode in that it's entirely dedicated to talking about both sides of that equation. We discuss: - The infinite demand for the newest models - SemiAnalysis going from $10K on AI spend to $7M - Mythos and Anthropic's compute problem - Why TSMC spending $100B on CapEx could cause a shortage - Robotics as next demand wave - Why memory prices will double again This is my second conversation with Dylan and find myself needing to speak with him more and more often to make sense of it all. Enjoy! Timestamps: 0:00 Intro 1:00 Surging AI Spend 10:27 Token Demand 16:21 When Ideas Are Cheap and Execution is Easy 20:46 Model Hoarding 22:34 Robotics 27:03 The Compute Bottleneck 30:26 The AI Permanent Underclass 31:39 Supply Chain Reality 37:47 CPUs 42:54 Predictions: Public Backlash
45:33

Every conversation I have with Dylan Patel, I'm really just trying to understand the supply and demand of tokens. This is a unique episode in that it's entirely dedicated to talking about both sides of that equation. We discuss: - The infinite demand for the newest models - SemiAnalysis going from $10K on AI spend to $7M - Mythos and Anthropic's compute problem - Why TSMC spending $100B on CapEx could cause a shortage - Robotics as next demand wave - Why memory prices will double again This is my second conversation with Dylan and find myself needing to speak with him more and more often to make sense of it all. Enjoy! Timestamps: 0:00 Intro 1:00 Surging AI Spend 10:27 Token Demand 16:21 When Ideas Are Cheap and Execution is Easy 20:46 Model Hoarding 22:34 Robotics 27:03 The Compute Bottleneck 30:26 The AI Permanent Underclass 31:39 Supply Chain Reality 37:47 CPUs 42:54 Predictions: Public Backlash

Patrick OShaughnessy

893,210 просмотров • 4 месяцев назад

Sam Altman on Sora, Energy, and Building an AI Empire From GPT-5 to Sora, OpenAI has been making a dizzying amount of bets recently. It now acts as a frontier AGI research lab, a big tech product company with nearly a billion users, and a driver of the largest infrastructure buildout in history, all in one - plus, it’s made partnerships with Microsoft, Nvidia, Oracle, AMD, SoftBank, Oracle, and even the US government. We talked to Sam Altman about the core thesis bringing OpenAI’s bets together, why they released Sora, how they use models internally, the most informative evals for assessing AI progress, and where we’re going from here. 00:00 Introduction 00:41 OpenAI’s vision 08:01 Human-AI interfaces 09:28 AI scientists and scientific progress 17:52 Sam's operating lessons 28:33 Copyright and open source 43:03 AI talent wars 45:33 Advice for founders Sam Altman benahorowitz.eth Erik Torenberg
49:26

Sam Altman on Sora, Energy, and Building an AI Empire From GPT-5 to Sora, OpenAI has been making a dizzying amount of bets recently. It now acts as a frontier AGI research lab, a big tech product company with nearly a billion users, and a driver of the largest infrastructure buildout in history, all in one - plus, it’s made partnerships with Microsoft, Nvidia, Oracle, AMD, SoftBank, Oracle, and even the US government. We talked to Sam Altman about the core thesis bringing OpenAI’s bets together, why they released Sora, how they use models internally, the most informative evals for assessing AI progress, and where we’re going from here. 00:00 Introduction 00:41 OpenAI’s vision 08:01 Human-AI interfaces 09:28 AI scientists and scientific progress 17:52 Sam's operating lessons 28:33 Copyright and open source 43:03 AI talent wars 45:33 Advice for founders Sam Altman benahorowitz.eth Erik Torenberg

a16z

1,301,404 просмотров • 10 месяцев назад