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Jacob Effron

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Managing Director @redpoint supporting @AbridgeHQ @wearelegora @tryaugie @tryramp @getgarner @AcuityMD @scribehow / AI pod: Unsupervised Learning

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It’s hard to imagine more of a dream Unsupervised Learning guest than Yann LeCun. Yann is one of the godfathers of AI, and he has some fascinating contrarian views on the limitations of LLMs. It was incredible to get to have a wide-ranging discussion with Yann about these views, reflections on his time at Meta and departure and what’s next for him. We hit on: ▪️ LLM limitations and a path forward for robotics ▪️ Why he left Meta ▪️ How he came to so dramatically disagree with his Turing co-laureates Geoff Hinton and Yoshua Bengio on LLMs ▪️ His predictions for 2027 ▪️ His new company AMI and the bet on world models ▪️ Why he compares OpenAI and Anthropic to Sun Microsystems ▪️ Why he tells PhD students to stop working on LLMs Plus some sharp views on the current safety discourse, how breakthrough research actually happens and what FAIR got right and wrong. YouTube: Spotify: Apple:

It’s hard to imagine more of a dream Unsupervised Learning guest than Yann LeCun. Yann is one of the godfathers of AI, and he has some fascinating contrarian views on the limitations of LLMs. It was incredible to get to have a wide-ranging discussion with Yann about these views, reflections on his time at Meta and departure and what’s next for him. We hit on: ▪️ LLM limitations and a path forward for robotics ▪️ Why he left Meta ▪️ How he came to so dramatically disagree with his Turing co-laureates Geoff Hinton and Yoshua Bengio on LLMs ▪️ His predictions for 2027 ▪️ His new company AMI and the bet on world models ▪️ Why he compares OpenAI and Anthropic to Sun Microsystems ▪️ Why he tells PhD students to stop working on LLMs Plus some sharp views on the current safety discourse, how breakthrough research actually happens and what FAIR got right and wrong. YouTube: Spotify: Apple:

190,835 просмотров • 21 дней назад

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

137,574 просмотров • 1 месяц назад

“LLMs are intrinsically unsafe.” Yann LeCun argues that models that can’t predict the consequences of their actions can’t be made reliable.

“LLMs are intrinsically unsafe.” Yann LeCun argues that models that can’t predict the consequences of their actions can’t be made reliable.

42,242 просмотров • 17 дней назад

.swyx 🌉 current thesis: "2025 was coding agents. 2026 is coding agents breaking containment to do everything else."

.swyx 🌉 current thesis: "2025 was coding agents. 2026 is coding agents breaking containment to do everything else."

18,374 просмотров • 1 месяц назад

Legora sets the bar for operating at AI speed. Watching them become one of the fastest growing software companies of all time these past few years has provided constant lessons on what’s required to win in this new world. Fresh off Legora's $550M Series D, CEO Max Junestrand joined logan bartlett and me on Unsupervised Learning to provide a masterclass on building an AI-native company. He shared some amazing lessons around - Constantly rebuilding for the bleeding edge of model capabilities - Partnering with customers for both immediate impact and long-term transformation - Running Legora differently from traditional software companies He also included some spicy takes on - Why foundation models entering legal is good for Legora - Pricing AI products - The future of the legal industry It’s impossible to listen to Max and not pick up the infectious energy that makes Legora such a special company. Check out the full episode: YouTube: Spotify: Apple: 0:00 Intro 1:16 Legora’s Series D Story 3:24 Why You Need Low Ego to Build in AI 5:58 From 60% to 100% Accuracy in One Summer 7:04 Law Firm Economics Shift 14:09 Pricing Seats Vs Outcomes 18:31 Why Foundation Models Entering Legal Helps Legora 30:10 Convincing a 75-Year-Old Partner to Go All In 33:02 Hiring Legal Engineers 34:32 Running an AI-Native Company 35:57 The Opus 4.5 Christmas Breakthrough 40:02 Building With Customers 44:01 All In On US Expansion 51:22 Stockholm Startup DNA

Legora sets the bar for operating at AI speed. Watching them become one of the fastest growing software companies of all time these past few years has provided constant lessons on what’s required to win in this new world. Fresh off Legora's $550M Series D, CEO Max Junestrand joined logan bartlett and me on Unsupervised Learning to provide a masterclass on building an AI-native company. He shared some amazing lessons around - Constantly rebuilding for the bleeding edge of model capabilities - Partnering with customers for both immediate impact and long-term transformation - Running Legora differently from traditional software companies He also included some spicy takes on - Why foundation models entering legal is good for Legora - Pricing AI products - The future of the legal industry It’s impossible to listen to Max and not pick up the infectious energy that makes Legora such a special company. Check out the full episode: YouTube: Spotify: Apple: 0:00 Intro 1:16 Legora’s Series D Story 3:24 Why You Need Low Ego to Build in AI 5:58 From 60% to 100% Accuracy in One Summer 7:04 Law Firm Economics Shift 14:09 Pricing Seats Vs Outcomes 18:31 Why Foundation Models Entering Legal Helps Legora 30:10 Convincing a 75-Year-Old Partner to Go All In 33:02 Hiring Legal Engineers 34:32 Running an AI-Native Company 35:57 The Opus 4.5 Christmas Breakthrough 40:02 Building With Customers 44:01 All In On US Expansion 51:22 Stockholm Startup DNA

31,921 просмотров • 2 месяцев назад

Always enjoy getting to chat with swyx 🇸🇬 AIE Singapore! on our annual cross-episode with Latent.Space on the state of AI. We hit on what’s shifted, what surprised us and what’s next. We covered: ▪️ Whether AI infrastructure has finally stabilized ▪️ Implications of agents buying developer tools ▪️ The AI coding wars ▪️ The foundation model vibe shift ▪️ Why Swyx reversed his view on open models ▪️ When to train your own model ▪️ What's top of mind for the best AI engineers YouTube: Spotify: Apple: 0:00 Intro 1:17 What the Top AI Engineers Are Thinking About 2:13 Has AI Infra Finally Stabilized? 6:39 When Does Doing RL In-House Make Sense? 11:26 Why Selling Dev Tools to Agents is Different 17:18 AI Coding Wars 29:04 Consumer AI Plateau 30:22 Codex vs Claude Code 44:52 Future of Open Models

Always enjoy getting to chat with swyx 🇸🇬 AIE Singapore! on our annual cross-episode with Latent.Space on the state of AI. We hit on what’s shifted, what surprised us and what’s next. We covered: ▪️ Whether AI infrastructure has finally stabilized ▪️ Implications of agents buying developer tools ▪️ The AI coding wars ▪️ The foundation model vibe shift ▪️ Why Swyx reversed his view on open models ▪️ When to train your own model ▪️ What's top of mind for the best AI engineers YouTube: Spotify: Apple: 0:00 Intro 1:17 What the Top AI Engineers Are Thinking About 2:13 Has AI Infra Finally Stabilized? 6:39 When Does Doing RL In-House Make Sense? 11:26 Why Selling Dev Tools to Agents is Different 17:18 AI Coding Wars 29:04 Consumer AI Plateau 30:22 Codex vs Claude Code 44:52 Future of Open Models

18,282 просмотров • 1 месяц назад

.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:

.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:

43,556 просмотров • 4 месяцев назад

.swyx 🌉 on whether AI infrastructure has finally stabilized:

.swyx 🌉 on whether AI infrastructure has finally stabilized:

12,420 просмотров • 1 месяц назад

OpenAI's Chief Scientist, Jakub Pachocki, on the continual learning wave: frontier labs are already building this into the core of the technology. The entire premise of scaling was to create systems that learn in context. Jakub says continual learning is not some separate missing piece, but “exactly what we’re working toward.”

OpenAI's Chief Scientist, Jakub Pachocki, on the continual learning wave: frontier labs are already building this into the core of the technology. The entire premise of scaling was to create systems that learn in context. Jakub says continual learning is not some separate missing piece, but “exactly what we’re working toward.”

15,065 просмотров • 1 месяц назад

ChinaTalk's Jordan Schneider came on Unsupervised Learning this week in an episode 15+ years in the making. Jordan is my go-to expert on all things China x AI. His hugely popular newsletter and podcast cover China's technology ecosystem, geopolitics, and economic policy. And we've been good friends since college. Some of my favorite parts of this episode include: - The state of the US-China AI race - The Chinese ecosystem pre and post-ChatGPT - Who China’s top AI players are - The strategic logic behind Chinese companies open-sourcing models - Chinese government AI policy - Why US chip export controls are only partially effective Check out the full episode ⬇️ YouTube: Spotify: Apple:

ChinaTalk's Jordan Schneider came on Unsupervised Learning this week in an episode 15+ years in the making. Jordan is my go-to expert on all things China x AI. His hugely popular newsletter and podcast cover China's technology ecosystem, geopolitics, and economic policy. And we've been good friends since college. Some of my favorite parts of this episode include: - The state of the US-China AI race - The Chinese ecosystem pre and post-ChatGPT - Who China’s top AI players are - The strategic logic behind Chinese companies open-sourcing models - Chinese government AI policy - Why US chip export controls are only partially effective Check out the full episode ⬇️ YouTube: Spotify: Apple:

13,081 просмотров • 6 месяцев назад

New Unsupervised Learning with Karol Hausman & Danny Driess (Physical Intelligence) on building generalist robotics foundation models and: - What’s next in AI x robotics - Biggest outstanding questions - How they 10x’d model training speed - Open sourcing π 0 - Breakthroughs in generalization Spotify: Apple: YouTube:

New Unsupervised Learning with Karol Hausman & Danny Driess (Physical Intelligence) on building generalist robotics foundation models and: - What’s next in AI x robotics - Biggest outstanding questions - How they 10x’d model training speed - Open sourcing π 0 - Breakthroughs in generalization Spotify: Apple: YouTube:

13,961 просмотров • 11 месяцев назад

New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source

New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source

12,351 просмотров • 9 месяцев назад

(1/20) Redpoint is co-leading Abridge’s $150M Series C! Abridge is the leading generative AI company for clinical documentation. Couldn’t be more thrilled to partner with Shiv Rao, MD, Zachary Lipton, @JuliaChapin and the entire team. ⬇️More on why we’re so excited:

(1/20) Redpoint is co-leading Abridge’s $150M Series C! Abridge is the leading generative AI company for clinical documentation. Couldn’t be more thrilled to partner with Shiv Rao, MD, Zachary Lipton, @JuliaChapin and the entire team. ⬇️More on why we’re so excited:

21,501 просмотров • 2 лет назад

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