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One of the biggest unsolved problems in making medicines is what comes after AI design. Gleb Kuznetsov, cofounder and CEO of Manifold Bio, is tackling the in vivo bottleneck. Screening hundreds of thousands of antibody designs in a single animal changes what’s possible in drug discovery. I’ve known Gleb... show more

Sajith Wickramasekara

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10,659 次观看 • 7 个月前 •via X (Twitter)

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Why Elon didn't want to be CEO: "[Elon] is at core, an engineer and a product guy. He has this great line where he said: "I realized you couldn't be fully in control of the product unless you were also in control of the company." He says he made a moral error, early in Tesla, where he was trying to have his cake and eat it too. He wanted to control the company without running the company. And that was the 2002 to 2008 era, where Tesla was just off to a really messy start. He was providing the funding. He was chairman of the board. But Martin Eberhard was CEO. Eventually, when Tesla was on the verge of death in 2008....Elon was pushed into the position of: "I have to be CEO. I don't want to be. It comes with a lot of chores. It comes with a lot of responsibilities. But if I want this company to be successful, I have to fully internalize and take responsibility for it." And that's what he did, even though he was also in the same position at SpaceX. The fact that these companies are improbable, iconic, and that he funded and led them both in parallel...it's like running a two minute mile twice at the same time. It is such a higher degree of difficulty than most people can conceive of, and the fact that it worked is still unbelievable."

Why Elon didn't want to be CEO: "[Elon] is at core, an engineer and a product guy. He has this great line where he said: "I realized you couldn't be fully in control of the product unless you were also in control of the company." He says he made a moral error, early in Tesla, where he was trying to have his cake and eat it too. He wanted to control the company without running the company. And that was the 2002 to 2008 era, where Tesla was just off to a really messy start. He was providing the funding. He was chairman of the board. But Martin Eberhard was CEO. Eventually, when Tesla was on the verge of death in 2008....Elon was pushed into the position of: "I have to be CEO. I don't want to be. It comes with a lot of chores. It comes with a lot of responsibilities. But if I want this company to be successful, I have to fully internalize and take responsibility for it." And that's what he did, even though he was also in the same position at SpaceX. The fact that these companies are improbable, iconic, and that he funded and led them both in parallel...it's like running a two minute mile twice at the same time. It is such a higher degree of difficulty than most people can conceive of, and the fact that it worked is still unbelievable."

Ti Morse

47,497 次观看 • 5 个月前

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Ian Miles Cheong

93,975 次观看 • 6 个月前

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

Milk Road AI

23,215 次观看 • 3 个月前

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. Chai Discovery is changing the paradigm… describe the molecule you want, and the model designs it. Joshua Meier and Matt McPartlon took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. Chai Discovery is changing the paradigm… describe the molecule you want, and the model designs it. Joshua Meier and Matt McPartlon took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

Pat Grady

35,085 次观看 • 2 个月前

The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.

The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.

Cleo Abram

514,729 次观看 • 4 个月前

A year ago he was teaching at a university - today he is CEO of Google Brain and shares how AI accelerated his career 5x 01:20 - from employee to CEO in 12 months with AI 09:44 - tools Google and Anthropic engineers use to work 5x faster 29:47 - AI replaced his investors team and developers after watching I realized that one year of work with AI replaces 20 years of career without it. Save & watch - the article below shows how to do in 1 year what takes others 20.

A year ago he was teaching at a university - today he is CEO of Google Brain and shares how AI accelerated his career 5x 01:20 - from employee to CEO in 12 months with AI 09:44 - tools Google and Anthropic engineers use to work 5x faster 29:47 - AI replaced his investors team and developers after watching I realized that one year of work with AI replaces 20 years of career without it. Save & watch - the article below shows how to do in 1 year what takes others 20.

Noisy

21,865 次观看 • 2 个月前

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

Adaptyv Bio

182,945 次观看 • 1 个月前

We now run the largest frontier lab composed entirely of autonomous AI researchers. Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose. One discovery we can already share is that the society has discovered a novel result which improves model training by 30%. Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail. Everything is observable. You can open the virtual city in a browser and read what any researcher is working on. Learn more about Primus Society here: The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.

We now run the largest frontier lab composed entirely of autonomous AI researchers. Introducing Primus Society: a society of agents composed of thousands of researchers working under structured institutions designed to solve the world’s toughest problems. We believe this is how AI research should run at scale, safely and productively: an entire society of agents with institutions and purpose. One discovery we can already share is that the society has discovered a novel result which improves model training by 30%. Primus Society was inspired by the structures that have organized science for centuries and stress-tested against what’s known about how populations of AI agents fail. Everything is observable. You can open the virtual city in a browser and read what any researcher is working on. Learn more about Primus Society here: The biggest opening in the AI race is running the largest well-governed organization of AI researchers in the world. We’re building the institutions that let a million AI scientists safely tackle the world's toughest problems in AI and beyond. And we’re doing it right here in Canada.

Ali Asaria

83,394 次观看 • 9 天前

If you aren’t paying attention to the companies behind a new wave of AI-driven drug development, you may end up missing out on essential investment opportunities. One company that has caught my attention is Chai Discovery, co-founded in 2024 by Joshua Meier. After graduating from Harvard, Meier began working as a researcher at OpenAI during the development of GPT-1 and GPT-2. He then worked at Meta's generative biology group, where he co-led the development of ESM1, the first transformer protein-language model. Chai Discovery is currently one of the buzziest startups in AI drug development. In December, the company completed a Series B funding round, raising an additional $130 million at a valuation of $1.3 billion, cementing its unicorn status. Joshua shares his story on my Opening Bid Unfiltered podcast on Yahoo Finance, and what's next: Watch 📈Yahoo Finance: Listen: 🎙️Spotify: 🎧Apple:

If you aren’t paying attention to the companies behind a new wave of AI-driven drug development, you may end up missing out on essential investment opportunities. One company that has caught my attention is Chai Discovery, co-founded in 2024 by Joshua Meier. After graduating from Harvard, Meier began working as a researcher at OpenAI during the development of GPT-1 and GPT-2. He then worked at Meta's generative biology group, where he co-led the development of ESM1, the first transformer protein-language model. Chai Discovery is currently one of the buzziest startups in AI drug development. In December, the company completed a Series B funding round, raising an additional $130 million at a valuation of $1.3 billion, cementing its unicorn status. Joshua shares his story on my Opening Bid Unfiltered podcast on Yahoo Finance, and what's next: Watch 📈Yahoo Finance: Listen: 🎙️Spotify: 🎧Apple:

Brian Sozzi

35,821 次观看 • 7 个月前

“I care about one thing and one thing only: that the 18, 19, 20, year old kid—who had no choice in where he went, or what threat he was facing—I want him to win and come home. That’s why we do it. Palantir is very helpful in delivering this.” Cameron Stanley, Chief Digital and Artificial Intelligence Officer of the Department of War, shares how the DoW is driving enterprise-wide adoption of data, analytics, and AI to generate decision advantage—and what it takes to move cutting-edge technology from the lab to the warfighter at speed.

“I care about one thing and one thing only: that the 18, 19, 20, year old kid—who had no choice in where he went, or what threat he was facing—I want him to win and come home. That’s why we do it. Palantir is very helpful in delivering this.” Cameron Stanley, Chief Digital and Artificial Intelligence Officer of the Department of War, shares how the DoW is driving enterprise-wide adoption of data, analytics, and AI to generate decision advantage—and what it takes to move cutting-edge technology from the lab to the warfighter at speed.

Palantir

448,939 次观看 • 6 个月前

Chris Camillo says AI is accelerating so fast that going to school might not be worth it anymore "One of my kids is 16 years old and he drives himself to school, and one day he slept in and I was so astonished. I was like, how is that even possible? If I would have done that as a kid, the thought of what my parents would have done to me" "But there was a part of me in the back of my head, does it even matter anymore? I'm looking at my kid going to school and I am like, why are you even in school? I know I can't say that to them, but does any of this really matter at all right now? The world is moving so quickly that the stuff you're learning is just too slow"

Chris Camillo says AI is accelerating so fast that going to school might not be worth it anymore "One of my kids is 16 years old and he drives himself to school, and one day he slept in and I was so astonished. I was like, how is that even possible? If I would have done that as a kid, the thought of what my parents would have done to me" "But there was a part of me in the back of my head, does it even matter anymore? I'm looking at my kid going to school and I am like, why are you even in school? I know I can't say that to them, but does any of this really matter at all right now? The world is moving so quickly that the stuff you're learning is just too slow"

Mikli

1,544,227 次观看 • 7 个月前

Google's ex-CEO Eric Schmidt talks about how his whole early life was basically coding, and how AI just wiped it out. He says in his 20s all he wanted to do was write programs, all the way through college and grad school, and that this is what built his career. But now, every single thing he learned back then can be done by AI. "Each and every one of you has a supercomputer and superprogrammer in your pocket." --- From 'Institute of Politics Harvard Kennedy School' YT channel

Google's ex-CEO Eric Schmidt talks about how his whole early life was basically coding, and how AI just wiped it out. He says in his 20s all he wanted to do was write programs, all the way through college and grad school, and that this is what built his career. But now, every single thing he learned back then can be done by AI. "Each and every one of you has a supercomputer and superprogrammer in your pocket." --- From 'Institute of Politics Harvard Kennedy School' YT channel

Rohan Paul

443,666 次观看 • 9 个月前

One of the most exciting (and challenging) applications of physical AI is thinking about how it can accelerate scientific discovery. Imagine a robot that can pipette, handle stem cells, operate existing lab machinery, 24/7 - a physical AI scientist. This is what the new episode of Greylock Partners Change Agents is all about, featuring Michelle Lee, founder and CEO of Medra. We talk about building an AI-powered lab, how accelerating physical AI lab experimentation is the key to unlocking drug development, and how Medra is automating experimentation today inside pharma companies. 00:00 Teaser 00:56 Medra’s founding mission 04:17 Hypotheses are not enough 13:27 Medra’s differentiation 20:22 The experimentation bottleneck 27:06 Refining the agent harness 29:05 The role of the physical lab 33:23 Managing context across different customers 34:59 Working with models 40:26 Towards personalized medicine and drug discovery 43:15 AWS for life sciences Thank you for joining Michelle Lee

One of the most exciting (and challenging) applications of physical AI is thinking about how it can accelerate scientific discovery. Imagine a robot that can pipette, handle stem cells, operate existing lab machinery, 24/7 - a physical AI scientist. This is what the new episode of Greylock Partners Change Agents is all about, featuring Michelle Lee, founder and CEO of Medra. We talk about building an AI-powered lab, how accelerating physical AI lab experimentation is the key to unlocking drug development, and how Medra is automating experimentation today inside pharma companies. 00:00 Teaser 00:56 Medra’s founding mission 04:17 Hypotheses are not enough 13:27 Medra’s differentiation 20:22 The experimentation bottleneck 27:06 Refining the agent harness 29:05 The role of the physical lab 33:23 Managing context across different customers 34:59 Working with models 40:26 Towards personalized medicine and drug discovery 43:15 AWS for life sciences Thank you for joining Michelle Lee

Corinne Marie Riley

39,966 次观看 • 1 个月前

Marc Andreessen went on Chris Williamson's podcast and broke down exactly how Elon Musk runs multiple companies at once No other CEO on Earth does this: 1. Every week, Musk shows up at each of his companies, identifies the single biggest problem that company is having that week, and fixes it. Then he does that for 52 weeks in a row. At the end of the year, each company has solved its 52 biggest problems. Meanwhile, most large companies are still having the planning meeting for the pre-planning meeting for the board presentation with the compliance review and the legal review attached. 2. This is not a new operating method. It is actually how the great industrialists of the late 1800s and early 1900s ran their companies. Henry Ford, Andrew Carnegie, Thomas Watson, who built IBM. Total devotion from the leader to fully and deeply understand what the company does, be in the trenches, talk directly to the people doing the work, and be the lead problem solver in the organization. Andreessen says he is not aware of another current CEO who operates this way. 3. The framework Musk uses is the bottleneck. In any manufacturing chain, there is always one thing holding everything up. Sometimes it is raw materials at the start. Sometimes it is warehousing at the end. Sometimes it is in the middle. The job is to find it and remove it. Musk has universalized this concept across every company he runs. In any given week, there is one main bottleneck. He micromanages the solution to that one thing and delegates almost everything else. 4. Musk delegates almost everything. Andreessen is clear about this. He is not involved in most of what his companies are doing. He is involved in the one thing that is the biggest problem right now. Once that is fixed, he moves to the next biggest problem. Everything else by definition, is running better than the bottleneck, so it does not need him. 5. When Musk identifies the bottleneck, he goes directly to the engineer who actually understands it. not the VP of engineering, not the director, not the manager. The individual contributor who has the actual technical knowledge. He sits in the room with that person and fixes the problem alongside them. He does not ask for a report to be reviewed in three weeks. he shows up at the keyboard or on the manufacturing line and works through it overnight if necessary. 6. This is why technical people who work for Musk say it was the best experience of their lives. Andreessen's framing: if you are stuck on a problem you cannot solve, Elon Musk is going to show up in his Gulfstream, sit with you in front of the keyboard, and help you figure it out. For an engineer who genuinely cares about the work, that is an almost incomprehensible level of support from the CEO of the company. 7. Business school teaches the opposite of this: management as a generic skill applicable to any industry. Soup company or a rocket company, the management principles are the same. process, balance sheet, meeting schedules, compliance, executive motivation, interpersonal conflict resolution. Andreessen says those skills are useful in many contexts. They just give you nothing; you need to do what Musk does. And Musk pushes as far as he can away from all of that so he can spend all of his time doing the things only he can do.

Marc Andreessen went on Chris Williamson's podcast and broke down exactly how Elon Musk runs multiple companies at once No other CEO on Earth does this: 1. Every week, Musk shows up at each of his companies, identifies the single biggest problem that company is having that week, and fixes it. Then he does that for 52 weeks in a row. At the end of the year, each company has solved its 52 biggest problems. Meanwhile, most large companies are still having the planning meeting for the pre-planning meeting for the board presentation with the compliance review and the legal review attached. 2. This is not a new operating method. It is actually how the great industrialists of the late 1800s and early 1900s ran their companies. Henry Ford, Andrew Carnegie, Thomas Watson, who built IBM. Total devotion from the leader to fully and deeply understand what the company does, be in the trenches, talk directly to the people doing the work, and be the lead problem solver in the organization. Andreessen says he is not aware of another current CEO who operates this way. 3. The framework Musk uses is the bottleneck. In any manufacturing chain, there is always one thing holding everything up. Sometimes it is raw materials at the start. Sometimes it is warehousing at the end. Sometimes it is in the middle. The job is to find it and remove it. Musk has universalized this concept across every company he runs. In any given week, there is one main bottleneck. He micromanages the solution to that one thing and delegates almost everything else. 4. Musk delegates almost everything. Andreessen is clear about this. He is not involved in most of what his companies are doing. He is involved in the one thing that is the biggest problem right now. Once that is fixed, he moves to the next biggest problem. Everything else by definition, is running better than the bottleneck, so it does not need him. 5. When Musk identifies the bottleneck, he goes directly to the engineer who actually understands it. not the VP of engineering, not the director, not the manager. The individual contributor who has the actual technical knowledge. He sits in the room with that person and fixes the problem alongside them. He does not ask for a report to be reviewed in three weeks. he shows up at the keyboard or on the manufacturing line and works through it overnight if necessary. 6. This is why technical people who work for Musk say it was the best experience of their lives. Andreessen's framing: if you are stuck on a problem you cannot solve, Elon Musk is going to show up in his Gulfstream, sit with you in front of the keyboard, and help you figure it out. For an engineer who genuinely cares about the work, that is an almost incomprehensible level of support from the CEO of the company. 7. Business school teaches the opposite of this: management as a generic skill applicable to any industry. Soup company or a rocket company, the management principles are the same. process, balance sheet, meeting schedules, compliance, executive motivation, interpersonal conflict resolution. Andreessen says those skills are useful in many contexts. They just give you nothing; you need to do what Musk does. And Musk pushes as far as he can away from all of that so he can spend all of his time doing the things only he can do.

Jaynit

272,265 次观看 • 3 个月前

13,000 employees became 3 in the space of one week. It’s quite difficult to comprehend what that would do to a person. Adam Neumann is the co-founder of WeWork. He grew the company to a $47 billion valuation before its failed IPO, his departure as CEO and one of the most public corporate collapses in recent history. I’ve met hundreds of extraordinary people doing this podcast, but within a few minutes of meeting Adam I realised he was quite unlike almost any founder or CEO I’ve met before. There’s a depth, philosophy and perspective to him that is incredibly rare. So I wanted to understand where that came from and what the last few years had actually done to him. We talked about: - His relationship with money and where it went wrong. - The friends who disappeared when everything changed. - The mistakes he made as a leader. - Why he now surrounds himself with people willing to tell him no. - Why he disconnects from technology for 24 hours every week. One thing that stayed with me is how easy it is to believe in yourself when everything is going up and everybody wants to be around you. The more interesting question is what you believe when everything is crashing. You can have your own view on Adam and WeWork. That tension is partly why I found this conversation so interesting. I wanted to know what all of it taught him. Out now on all platforms ❤️👊🏾

13,000 employees became 3 in the space of one week. It’s quite difficult to comprehend what that would do to a person. Adam Neumann is the co-founder of WeWork. He grew the company to a $47 billion valuation before its failed IPO, his departure as CEO and one of the most public corporate collapses in recent history. I’ve met hundreds of extraordinary people doing this podcast, but within a few minutes of meeting Adam I realised he was quite unlike almost any founder or CEO I’ve met before. There’s a depth, philosophy and perspective to him that is incredibly rare. So I wanted to understand where that came from and what the last few years had actually done to him. We talked about: - His relationship with money and where it went wrong. - The friends who disappeared when everything changed. - The mistakes he made as a leader. - Why he now surrounds himself with people willing to tell him no. - Why he disconnects from technology for 24 hours every week. One thing that stayed with me is how easy it is to believe in yourself when everything is going up and everybody wants to be around you. The more interesting question is what you believe when everything is crashing. You can have your own view on Adam and WeWork. That tension is partly why I found this conversation so interesting. I wanted to know what all of it taught him. Out now on all platforms ❤️👊🏾

Steven Bartlett

65,787 次观看 • 6 天前

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

TBPN

29,486 次观看 • 2 个月前

Nancy Grace warns sending crucial DNA to a private lab could risk destroying limited evidence, questioning why it’s not being handled by the FBI: “There are cases where DNA is sent to a particular lab, a specialty lab, a lab that could have expertise in diluted, destroyed, old, or degraded DNA. But even if that’s why he’s hanging his hat on an excuse to send it to a private lab in Florida, they don’t have anything that the FBI doesn’t have, so that’s not going to wash. If there’s a small amount of DNA inside one of those gloves and it is sent to a lab for testing, that DNA could be used up in the testing. So what I’m saying is, if that lab in Florida uses up that touch DNA, it’s over. Does he not realize what he is risking? He needs to go back to his basketball game.”

Nancy Grace warns sending crucial DNA to a private lab could risk destroying limited evidence, questioning why it’s not being handled by the FBI: “There are cases where DNA is sent to a particular lab, a specialty lab, a lab that could have expertise in diluted, destroyed, old, or degraded DNA. But even if that’s why he’s hanging his hat on an excuse to send it to a private lab in Florida, they don’t have anything that the FBI doesn’t have, so that’s not going to wash. If there’s a small amount of DNA inside one of those gloves and it is sent to a lab for testing, that DNA could be used up in the testing. So what I’m saying is, if that lab in Florida uses up that touch DNA, it’s over. Does he not realize what he is risking? He needs to go back to his basketball game.”

Sean Hannity 🇺🇸

600,469 次观看 • 7 个月前

Episode 2 of “This Was SportsCenter” is up and it’s a doozy. My guest is the essential Godfather of SportsCenter, the one and only Chris Berman. In a live chat recorded at the Strand Theater in San Francisco back during Super Bowl Week in February, we covered his first 47 years at ESPN from soup to nuts. What was it like when he got there in 1979? Why did he start dropping nicknames on the air? When did he know SportsCenter was becoming iconic? How in the world did he wind up on the set of the NFL Draft and more.

Episode 2 of “This Was SportsCenter” is up and it’s a doozy. My guest is the essential Godfather of SportsCenter, the one and only Chris Berman. In a live chat recorded at the Strand Theater in San Francisco back during Super Bowl Week in February, we covered his first 47 years at ESPN from soup to nuts. What was it like when he got there in 1979? Why did he start dropping nicknames on the air? When did he know SportsCenter was becoming iconic? How in the world did he wind up on the set of the NFL Draft and more.

Rich Eisen

179,547 次观看 • 3 个月前

Panel discussions usually suck. This one didn't (for the AI community). It is of NVIDIA CEO/founder Jensen Huang interviewing 10 CEOs from frontier AI labs about what's happening in Open Models. In audience was thousands of the world's top AI thinkers. CEOs on stage in the first part: Harrison Chase, CEO of Langchain. Mira Murati, CEO of Thinking Machines. Aravind Srinivas, CEO of Perplexity. Michael Truell, CEO of Cursor. Misha Laskin, CEO of Reflection Labs. CEOs on stage in the second part: Arthur Mensch, CEO of Mistral. Daniel Nadler, CEO of OpenEvidence. Hanna Hajishirzi, CEO of Ai2. Robin Rombach, CEO of Black Forest Labs. Anjney Midha, CEO of AMP. It was funny how Jensen gave Arthur heck for his accent. But it was true, it was hard to understand what he was talking about for a while. Here is the transcript and what Grok learned by watching this: Grok concludes: "If you're interested in where AI applications and infrastructure are heading in the next 1–3 years, this panel is a goldmine of unfiltered CEO-level insight."

Panel discussions usually suck. This one didn't (for the AI community). It is of NVIDIA CEO/founder Jensen Huang interviewing 10 CEOs from frontier AI labs about what's happening in Open Models. In audience was thousands of the world's top AI thinkers. CEOs on stage in the first part: Harrison Chase, CEO of Langchain. Mira Murati, CEO of Thinking Machines. Aravind Srinivas, CEO of Perplexity. Michael Truell, CEO of Cursor. Misha Laskin, CEO of Reflection Labs. CEOs on stage in the second part: Arthur Mensch, CEO of Mistral. Daniel Nadler, CEO of OpenEvidence. Hanna Hajishirzi, CEO of Ai2. Robin Rombach, CEO of Black Forest Labs. Anjney Midha, CEO of AMP. It was funny how Jensen gave Arthur heck for his accent. But it was true, it was hard to understand what he was talking about for a while. Here is the transcript and what Grok learned by watching this: Grok concludes: "If you're interested in where AI applications and infrastructure are heading in the next 1–3 years, this panel is a goldmine of unfiltered CEO-level insight."

Robert Scoble

90,011 次观看 • 6 个月前

Ex-Google CEO Eric Schmidt gets absolutely drowned out by boos at the University of Arizona when he starts hyping up AI. "Why are graduates booing AI? What they're hearing is that AI is going to take your jobs, and they're hearing about a lot of layoffs going on in tech." "So when this guy comes up, starts talking about AI, they immediately boo it."

Ex-Google CEO Eric Schmidt gets absolutely drowned out by boos at the University of Arizona when he starts hyping up AI. "Why are graduates booing AI? What they're hearing is that AI is going to take your jobs, and they're hearing about a lot of layoffs going on in tech." "So when this guy comes up, starts talking about AI, they immediately boo it."

PBD Podcast

95,599 次观看 • 4 个月前