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High-quality experimental data fuels biological discovery. Could an AI-enabled "autonomous lab" fundamentally increase the rate at which we generate it, and accelerate our progress toward cures for disease? That's the question I sat down to explore with Jason Kelly, CEO of Ginkgo Bioworks, for a deep-dive and tour of...

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Nabil Laoudji

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11,508 views • 5 months ago •via X (Twitter)

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Proud to announce that Ginkgo Bioworks was awarded a $47M contract to kick off President Trump's new Genesis Mission and build the world's largest autonomous lab at Pacific Northwest National Lab! I was honored to join Secretary Chris Wright as he commissioned the first phase of the lab yesterday at PNNL 🇺🇸🤖 Autonomous labs allow scientists to order experiments just by asking for them and are a key component for AI models to accelerate science at the scale outlined in the Genesis Project by Director Michael Kratsios, Dario Gil, Sriram Krishnan. More below, including some behind the scenes footage 😀
5:28

Proud to announce that Ginkgo Bioworks was awarded a $47M contract to kick off President Trump's new Genesis Mission and build the world's largest autonomous lab at Pacific Northwest National Lab! I was honored to join Secretary Chris Wright as he commissioned the first phase of the lab yesterday at PNNL 🇺🇸🤖 Autonomous labs allow scientists to order experiments just by asking for them and are a key component for AI models to accelerate science at the scale outlined in the Genesis Project by Director Michael Kratsios, Dario Gil, Sriram Krishnan. More below, including some behind the scenes footage 😀

Jason Kelly

223,889 views • 9 months ago

Awesome being on the Sequoia Capital Training Data podcast with Pat Grady and Sonya Huang 🐥. Topic links in the 🧵 The most human part of intelligence is curiosity, and science is formalized curiosity. If we want AGI — doing the work of science is the training set. Fun to talk history of Ginkgo Bioworks, our project with OpenAI where GPT-5 ran our Autonomous Lab, what new biotech apps are coming, and more. Also — you (or your moltbot) can order experiments from Ginkgo Bioworks Cloud Lab if you don't have your own lab or buy our robots to make your own autonomous lab. Technical details of our OpenAI partnership also in 🧵
1:00

Awesome being on the Sequoia Capital Training Data podcast with Pat Grady and Sonya Huang 🐥. Topic links in the 🧵 The most human part of intelligence is curiosity, and science is formalized curiosity. If we want AGI — doing the work of science is the training set. Fun to talk history of Ginkgo Bioworks, our project with OpenAI where GPT-5 ran our Autonomous Lab, what new biotech apps are coming, and more. Also — you (or your moltbot) can order experiments from Ginkgo Bioworks Cloud Lab if you don't have your own lab or buy our robots to make your own autonomous lab. Technical details of our OpenAI partnership also in 🧵

Jason Kelly

12,691 views • 6 months ago

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.
0:25

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

182,153 views • 1 month ago

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly
0:30

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

Adaptyv Bio

15,109 views • 3 months ago

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.
4:03

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.

Nima Alidoust

30,629 views • 2 months ago

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!
5:00

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!

Ginkgo Bioworks

50,223 views • 7 months ago

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.
1:44

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,867 views • 5 months ago

Priscilla Chan and Mark Zuckerberg on Curing All Disease We sat down with Dr. Priscilla Chan and Mark Zuckerberg, co-founders of the Chan Zuckerberg Initiative, to discuss their ambitious plan to cure, prevent, and manage all disease by the end of the century. CZI’s Biohub aims to generate vast biological datasets to train new frontier AI models to guide scientists in the lab, creating a feedback loop to accelerate the pace of basic science and take us towards a world without disease. 00:00 Introduction 03:42 Building tools to accelerate scientific discovery 07:03 Biohub = Frontier Biology + Frontier AI 13:32 "Most diseases should be thought of as rare diseases” 15:39 Inspiration: building a periodic table for biology 19:27 Why virtual cells? 21:17 The Biohub master plan 33:36 The importance of interface design in democratizing scientific discovery 35:34 How Biohub encourages cross-functional collaboration 40:38 Looking ahead: the broader impact of AI on biotech benahorowitz.eth Vineeta Agarwala Erik Torenberg
44:43

Priscilla Chan and Mark Zuckerberg on Curing All Disease We sat down with Dr. Priscilla Chan and Mark Zuckerberg, co-founders of the Chan Zuckerberg Initiative, to discuss their ambitious plan to cure, prevent, and manage all disease by the end of the century. CZI’s Biohub aims to generate vast biological datasets to train new frontier AI models to guide scientists in the lab, creating a feedback loop to accelerate the pace of basic science and take us towards a world without disease. 00:00 Introduction 03:42 Building tools to accelerate scientific discovery 07:03 Biohub = Frontier Biology + Frontier AI 13:32 "Most diseases should be thought of as rare diseases” 15:39 Inspiration: building a periodic table for biology 19:27 Why virtual cells? 21:17 The Biohub master plan 33:36 The importance of interface design in democratizing scientific discovery 35:34 How Biohub encourages cross-functional collaboration 40:38 Looking ahead: the broader impact of AI on biotech benahorowitz.eth Vineeta Agarwala Erik Torenberg

a16z

377,496 views • 10 months ago

Today, we released Microsoft's newest AI Diffusion Report, and I sat down with Juan M. Lavista Ferres, Chief Data Scientist and Director of the AI for Good Lab, for a special edition of #ToolsAndWeapons to unpack the report’s most striking insights. It’s a conversation about technology’s reach, the pace of progress, and what it means for the world. The report reveals how generative AI is spreading globally—and why the speed of diffusion will determine who benefits most. We spotlight countries leading the charge like UAE, Singapore, and South Korea, and explore the rise of models such as China’s DeepSeek across parts of Africa. Listen to the full episode:
1:10

Today, we released Microsoft's newest AI Diffusion Report, and I sat down with Juan M. Lavista Ferres, Chief Data Scientist and Director of the AI for Good Lab, for a special edition of #ToolsAndWeapons to unpack the report’s most striking insights. It’s a conversation about technology’s reach, the pace of progress, and what it means for the world. The report reveals how generative AI is spreading globally—and why the speed of diffusion will determine who benefits most. We spotlight countries leading the charge like UAE, Singapore, and South Korea, and explore the rise of models such as China’s DeepSeek across parts of Africa. Listen to the full episode:

Brad Smith

85,326 views • 8 months ago

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!
1:05

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

Bo Wang

27,656 views • 1 year ago

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
46:43

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

39,467 views • 22 days ago

Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!
0:59

Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

Cristian Ponce

1,140,918 views • 3 months ago

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉
6:30

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉

Karim Beguir

16,539 views • 1 year ago

Artificial intelligence supporting engineers, autonomous drones for weld inspection, AI assistants for translations... these and many other innovations come to life in the Digital Lab, our center of excellence for the adoption of the most advanced technologies. Join us in this new episode of “Insiders” to discover a place where AI, robotics and engineering converge around a single vision: building a new shipbuilding industry, with people always at its core. This video was translated with our AI ecosystem brAIn. Curious to see the original video in Italian? Join us on Instagram! 👉
3:06

Artificial intelligence supporting engineers, autonomous drones for weld inspection, AI assistants for translations... these and many other innovations come to life in the Digital Lab, our center of excellence for the adoption of the most advanced technologies. Join us in this new episode of “Insiders” to discover a place where AI, robotics and engineering converge around a single vision: building a new shipbuilding industry, with people always at its core. This video was translated with our AI ecosystem brAIn. Curious to see the original video in Italian? Join us on Instagram! 👉

Fincantieri

75,505,320 views • 6 months ago

🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:
0:58

🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:

Recursion

98,862 views • 2 months ago

Andrej Karpathy had already talked about this possibility 1 month back, that he might join one of the big AI labs and why he may do so. "They (the AI labs) are kind of like at the edge of capability of what's possible. And they are working on what's coming down the line. And I think if you're outside of that frontier lab, your judgment fundamentally will start to drift because you're not part of what's coming down the line. And so I feel like my judgment will inevitably start to drift as well. And I won't actually have an understanding of how these systems actually work under the hood. It's a big system. I won't have a good understanding of how to develop and etc. And so I do think that in that sense, I agree and something I'm nervous about. I think it's worth basically being in touch with what's actually happening and actually being in the frontier lab. And if some of the frontier labs would have me come for some amount of time and do really good work for them, then I think that's maybe a good setup" --- From 'No Priors: AI, Machine Learning, Tech, & Startups" YT channel (link in comment)
0:46

Andrej Karpathy had already talked about this possibility 1 month back, that he might join one of the big AI labs and why he may do so. "They (the AI labs) are kind of like at the edge of capability of what's possible. And they are working on what's coming down the line. And I think if you're outside of that frontier lab, your judgment fundamentally will start to drift because you're not part of what's coming down the line. And so I feel like my judgment will inevitably start to drift as well. And I won't actually have an understanding of how these systems actually work under the hood. It's a big system. I won't have a good understanding of how to develop and etc. And so I do think that in that sense, I agree and something I'm nervous about. I think it's worth basically being in touch with what's actually happening and actually being in the frontier lab. And if some of the frontier labs would have me come for some amount of time and do really good work for them, then I think that's maybe a good setup" --- From 'No Priors: AI, Machine Learning, Tech, & Startups" YT channel (link in comment)

Rohan Paul

57,496 views • 4 months ago

The German Federal Innovation Agency is running a €125m competition to fund 3 new Frontier AI labs in Europe! "Next Frontier AI" is a pan-European initiative led by SPRIND, Federal Agency for Breakthrough Innovation to create three globally relevant Frontier AI labs. It starts with a €125m challenge where up to 10 teams receive funding over 24 months to build frontier-lab capabilities, including team, infrastructure, prototypes, evaluation discipline, and early pilots. The best-performing teams will then receive structured support toward raising ~€1 billion each. Europe has some of the top AI companies in the world, but as a continent we are massively dependent on frontier labs from other countries. This initiative seeks to change that and I am HERE FOR IT.
1:30

The German Federal Innovation Agency is running a €125m competition to fund 3 new Frontier AI labs in Europe! "Next Frontier AI" is a pan-European initiative led by SPRIND, Federal Agency for Breakthrough Innovation to create three globally relevant Frontier AI labs. It starts with a €125m challenge where up to 10 teams receive funding over 24 months to build frontier-lab capabilities, including team, infrastructure, prototypes, evaluation discipline, and early pilots. The best-performing teams will then receive structured support toward raising ~€1 billion each. Europe has some of the top AI companies in the world, but as a continent we are massively dependent on frontier labs from other countries. This initiative seeks to change that and I am HERE FOR IT.

Seb Johnson

24,124 views • 5 months ago