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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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"As a proprietary model builder, you're kind of starting to get squeezed in.” That's Igor Babuschkin’s take on where the biggest AI labs stand today. He explains how you need the models to get way better to keep margin, but at some level they may be too sensitive to release. This week, I sat down with Igor on Unsupervised Learning. It was a fascinating conversation with someone who has real perspective on the questions everyone in AI is asking right now. From DeepMind's StarCraft project to early reasoning work at OpenAI to co-founding xAI, Igor has had a front-row seat to nearly every major AI breakthrough. He's now the co-founder of River AI, building individualized, locally-run AI models. We discuss: ▪️Why proprietary model labs might be in trouble ▪️What it's like working with Elon ▪️Building Colossus in 120 days ▪️Should enterprises train their own models ▪️What’s left for humans as models get better ▪️Why he left xAI to bet on personal local AI instead ▪️The three bets River is taking ▪️What's stopping AI from moving beyond coding ▪️Reflections on the rapid pace of the past years 0:00 Intro 1:17 Writing Fiction on Where AI Is Headed 4:46 Cracking Agents Beyond Coding 10:29 Why Igor Left to Start River 12:22 River's Three Big Bets 18:06 Weights vs. Memory: The Personalization Debate 22:04 Should Enterprises Train Their Own Models? 25:10 Are Proprietary Labs Losing Their Edge? 32:16 The China Open-Source Problem 44:19 The Elon Call That Started xAI 50:18 Thoughts on Cursor Acquisition 52:16 What's Actually Bottlenecking AI 56:55 Humans, Machines, and Staying Relevant 1:01:29 Igor's Odds This All Goes Well YouTube: Spotify: Apple:

"As a proprietary model builder, you're kind of starting to get squeezed in.” That's Igor Babuschkin’s take on where the biggest AI labs stand today. He explains how you need the models to get way better to keep margin, but at some level they may be too sensitive to release. This week, I sat down with Igor on Unsupervised Learning. It was a fascinating conversation with someone who has real perspective on the questions everyone in AI is asking right now. From DeepMind's StarCraft project to early reasoning work at OpenAI to co-founding xAI, Igor has had a front-row seat to nearly every major AI breakthrough. He's now the co-founder of River AI, building individualized, locally-run AI models. We discuss: ▪️Why proprietary model labs might be in trouble ▪️What it's like working with Elon ▪️Building Colossus in 120 days ▪️Should enterprises train their own models ▪️What’s left for humans as models get better ▪️Why he left xAI to bet on personal local AI instead ▪️The three bets River is taking ▪️What's stopping AI from moving beyond coding ▪️Reflections on the rapid pace of the past years 0:00 Intro 1:17 Writing Fiction on Where AI Is Headed 4:46 Cracking Agents Beyond Coding 10:29 Why Igor Left to Start River 12:22 River's Three Big Bets 18:06 Weights vs. Memory: The Personalization Debate 22:04 Should Enterprises Train Their Own Models? 25:10 Are Proprietary Labs Losing Their Edge? 32:16 The China Open-Source Problem 44:19 The Elon Call That Started xAI 50:18 Thoughts on Cursor Acquisition 52:16 What's Actually Bottlenecking AI 56:55 Humans, Machines, and Staying Relevant 1:01:29 Igor's Odds This All Goes Well YouTube: Spotify: Apple:

167,826 Aufrufe • vor 1 Monat

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:

200,587 Aufrufe • vor 3 Monaten

.Jürgen Schmidhuber on why no AI lab can build a durable moat around recursive self-improvement

.Jürgen Schmidhuber on why no AI lab can build a durable moat around recursive self-improvement

95,059 Aufrufe • vor 1 Monat

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

138,391 Aufrufe • vor 5 Monaten

I sat down with Lukasz Kaiser to get into whether the architecture he helped invent is actually enough, and what's next in generalization, coding agents, RL and more. Lukasz co-authored "Attention Is All You Need," the paper that introduced the transformer and worked on reasoning models at OpenAI so he’s been a key part of major shifts in the field. We hit on: ▪️ The case for and against a new architecture coming after the transformer ▪️ What’s required for model generalization in the physical world ▪️ How much coding agents have improved his AI research productivity ▪️ The next domains for RL ▪️ Why Anthropic initially won coding ▪️ Future research directions he’s excited about 0:00 Intro 1:12 Transformers vs. Human Learning 8:37 How Do We Get Physical World Generalization? 10:52 What Comes After Transformers 13:59 How Much Have Agents Improved Lukasz's AI Research Productivity? 17:21 How Close Is an AI Research Intern? 26:06 RL Beyond Verifiable Tasks 35:38 App Companies: Build Models or Lean on Labs? 46:21 Multimodal Is Still Missing Something 49:46 OpenAI's Bet on Reasoning 55:26 The AI Coding Wars 59:26 Focus vs. Keeping Embers Burning 1:02:09 Open Source vs. Closed Source Gap 1:05:15 Quickfire YouTube: Spotify: Apple:

I sat down with Lukasz Kaiser to get into whether the architecture he helped invent is actually enough, and what's next in generalization, coding agents, RL and more. Lukasz co-authored "Attention Is All You Need," the paper that introduced the transformer and worked on reasoning models at OpenAI so he’s been a key part of major shifts in the field. We hit on: ▪️ The case for and against a new architecture coming after the transformer ▪️ What’s required for model generalization in the physical world ▪️ How much coding agents have improved his AI research productivity ▪️ The next domains for RL ▪️ Why Anthropic initially won coding ▪️ Future research directions he’s excited about 0:00 Intro 1:12 Transformers vs. Human Learning 8:37 How Do We Get Physical World Generalization? 10:52 What Comes After Transformers 13:59 How Much Have Agents Improved Lukasz's AI Research Productivity? 17:21 How Close Is an AI Research Intern? 26:06 RL Beyond Verifiable Tasks 35:38 App Companies: Build Models or Lean on Labs? 46:21 Multimodal Is Still Missing Something 49:46 OpenAI's Bet on Reasoning 55:26 The AI Coding Wars 59:26 Focus vs. Keeping Embers Burning 1:02:09 Open Source vs. Closed Source Gap 1:05:15 Quickfire YouTube: Spotify: Apple:

75,222 Aufrufe • vor 3 Monaten

“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.

42,383 Aufrufe • vor 3 Monaten

Are Chinese open models being oversold as reaching the frontier? We’re back with another vibe check episode with Ari Morcos and Rob Toews. These are a ton of fun to record and this was a particularly meaty one given everything going on. We hit on: ▪️Did Chinese open models catch the US frontier? ▪️How much does distillation explain China's progress ▪️Reactions to Anthropic Backlash ▪️What a Stuxnet style backdoor in model weights looks like ▪️The OpenAI and Hugging Face breach ▪️xAI and Cursor ▪️Prediction check-ins 0:00 Intro 2:41 China's Open Source Models Catch Up 7:42 Does Distillation Explain China's Rise? 13:54 The Geopolitical Risk of Chinese AI Models 20:28 Should the Government Restrict Open Models? 22:06 What Are the Labs Really Learning From You? 29:22 Future of Government Regulation 40:50 The OpenAI-Hugging Face Hack 47:59 Grok, Cursor, and the Value of Real Data 56:52 SSI and OpenRouter 1:02:52 Venture Capital's Return to Deep Tech 1:05:16 Quickfire YouTube: Spotify: Apple:

Are Chinese open models being oversold as reaching the frontier? We’re back with another vibe check episode with Ari Morcos and Rob Toews. These are a ton of fun to record and this was a particularly meaty one given everything going on. We hit on: ▪️Did Chinese open models catch the US frontier? ▪️How much does distillation explain China's progress ▪️Reactions to Anthropic Backlash ▪️What a Stuxnet style backdoor in model weights looks like ▪️The OpenAI and Hugging Face breach ▪️xAI and Cursor ▪️Prediction check-ins 0:00 Intro 2:41 China's Open Source Models Catch Up 7:42 Does Distillation Explain China's Rise? 13:54 The Geopolitical Risk of Chinese AI Models 20:28 Should the Government Restrict Open Models? 22:06 What Are the Labs Really Learning From You? 29:22 Future of Government Regulation 40:50 The OpenAI-Hugging Face Hack 47:59 Grok, Cursor, and the Value of Real Data 56:52 SSI and OpenRouter 1:02:52 Venture Capital's Return to Deep Tech 1:05:16 Quickfire YouTube: Spotify: Apple:

14,496 Aufrufe • vor 1 Monat

.Sebastian Mallaby spent 30+ hours with Demis Hassabis in the back of a British pub. I sat down with Sebastian to chat about his recent book, The Infinity Machine, where he provides a fascinating history of DeepMind, Demis and the broader AI landscape. We hit on: ▪️ Whether the race between labs was inevitable ▪️ Project Mario: the secret plan to spin DeepMind out of Google ▪️ Why Google missed scaling the transformer despite inventing it and wasn’t first to ChatGPT and Claude Code ▪️ The personal history between Demis, Elon and Sam ▪️ The quasi-spiritual conviction Sebastian didn't expect to find underneath the science 0:00 Intro 2:04 Was the AI Race Inevitable? 4:03 The 2015 Safety Summit Backfire 11:27 Why Google Doesn't Make As Concentrated Bets 15:51 Project Mario: The Secret Spinout Plan 19:43 What Demis Actually Regrets 23:46 Venture Startups vs. Tech Behemoths 34:08 David Silver and the RL True Believers 38:21 Demis, Elon, and the Evil Genius Feud 42:39 Great Man Theory vs. Inevitability 45:00 What Demis Didn't Want Published YouTube: Spotify: Apple:

.Sebastian Mallaby spent 30+ hours with Demis Hassabis in the back of a British pub. I sat down with Sebastian to chat about his recent book, The Infinity Machine, where he provides a fascinating history of DeepMind, Demis and the broader AI landscape. We hit on: ▪️ Whether the race between labs was inevitable ▪️ Project Mario: the secret plan to spin DeepMind out of Google ▪️ Why Google missed scaling the transformer despite inventing it and wasn’t first to ChatGPT and Claude Code ▪️ The personal history between Demis, Elon and Sam ▪️ The quasi-spiritual conviction Sebastian didn't expect to find underneath the science 0:00 Intro 2:04 Was the AI Race Inevitable? 4:03 The 2015 Safety Summit Backfire 11:27 Why Google Doesn't Make As Concentrated Bets 15:51 Project Mario: The Secret Spinout Plan 19:43 What Demis Actually Regrets 23:46 Venture Startups vs. Tech Behemoths 34:08 David Silver and the RL True Believers 38:21 Demis, Elon, and the Evil Genius Feud 42:39 Great Man Theory vs. Inevitability 45:00 What Demis Didn't Want Published YouTube: Spotify: Apple:

31,357 Aufrufe • vor 3 Monaten

.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:

43,788 Aufrufe • vor 7 Monaten

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

32,227 Aufrufe • vor 6 Monaten

.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."

18,598 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 5 Monaten

.swyx 🌉 on whether AI infrastructure has finally stabilized:

.swyx 🌉 on whether AI infrastructure has finally stabilized:

12,420 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 9 Monaten

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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 1 Jahr

(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:

21,556 Aufrufe • vor 2 Jahren

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