As a three-time founder, Parker Conrad has one piece of advice for aspiring entrepreneurs—don’t do it. The Rippling co-founder and CEO joins Sarah Guo to talk about what he learned from the crash at Zenefits, why most advice to founders is wrong, and how building a real platform—not a point solution—is the only way to win in SaaS. The two get into founder psychology, the myth of learning from failure, and what true ownership looks like inside a company. He also shares why AI won’t shrink teams anytime soon, what people misunderstand about vertical software, and why ambition trumps efficiency with long-lasting companies. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @parkerconrad Chapters: 00:00 Introduction to Parker Conrad 00:33 Lessons from Zenefits to Rippling 01:54 The Psychology of Founding a Company 07:56 Rippling's Ambitious Vision 10:41 Building a Platform Company 15:05 Challenges and Strategies in Scaling 30:36 AI's Impact on Software Development 42:06 Public vs. Private: Rippling's Future 44:19 Conclusion

AI has already fueled breakthroughs in biotechnology—but now, further advances in AI are poised to fuel pharmaceutical discoveries as well. Sarah Guo sits down with Joshua Meier and Jack Dent, co-founders of Chai Discovery, whose newly launched Chai-2 designs bespoke antibodies that bind to their targets at a jaw-dropping 20% rate. Jack and Joshua talk about the implications for Chai-2’s success rate at discovering antibodies for the pharmaceutical industry, how structure prediction is pivotal in making the model work, and future potential for using the model to optimize other molecular properties. Plus, they talk about what they believe bioscientists should be learning to best utilize Chai-2’s technology.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @_jackdent | @joshim5 Chapters: 00:00 – Joshua Meier and Jack Dent Introduction 01:09 – Genesis of Chai Discovery 06:12 – Chai-2 Model 10:13 – Criteria for Specifying Targets for Chai-2 13:12 – How the Chai-2 Model Works 16:12 – Emergent Vocabulary from Chai-2 18:15 – Hopes for Chai-2’s Impact 20:33 – Reception of the Chai-2 Model 22:16 – Future of Wet Lab Screening and Biotech 27:08 – Optimizing Other Molecule Properties 31:37 – Where Chai Invests From Here 36:20 – What Bioscientists Should Learn for Chai-2 40:23 – How Jack and Josh Oriented to the Biotech Space 43:38 – Platform Investment and Chai-2 46:53 – Scaling Chai Discovery 48:21 – Hiring at Chai Discovery 49:09 – Conclusion

Much of the scientific process involves searching. But rather than continue to rely on the luck of discovery, Google DeepMind has engineered a more efficient AI agent that mines complex spaces to facilitate scientific breakthroughs. Sarah Guo speaks with Pushmeet Kohli, VP of Science and Strategic Initiatives, and research scientist Matej Balog at Google DeepMind about AlphaEvolve, an autonomous coding agent they developed that finds new algorithms through evolutionary search. Pushmeet and Matej talk about how AlphaEvolve tackles the problem of matrix multiplication efficiency, scaling and iteration in problem solving, and whether or not this means we are at self-improving AI. Together, they also explore the implications AlphaEvolve has to other sciences beyond mathematics and computer science. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @pushmeet | @matejbalog Chapters: 00:00 Pushmeet Kohli and Matej Balog Introduction 0:48 Origin of AlphaEvolve 02:31 AlphaEvolve’s Progression from AlphaGo and AlphaTensor 08:02 The Open Problem of Matrix Multiplication Efficiency 11:18 How AlphaEvolve Evolves Code 14:43 Scaling and Predicting Iterations 16:52 Implications for Coding Agents 19:42 Overcoming Limits of Automated Evaluators 25:21 Are We At Self-Improving AI? 28:10 Effects on Scientific Discovery and Mathematics 31:50 Role of Human Scientists with AlphaEvolve 38:30 Making AlphaEvolve Broadly Accessible 40:18 Applying AlphaEvolve Within Google 41:39 Conclusion

When will fully autonomous vehicles see widespread adoption? According to Applied Intuition, that future is closer than you may think. Applied Intuition’s CEO, Qasar Younis, and CTO, Peter Ludwig, talk with Elad Gil about how now is the best time to both work on self-driving vehicle technology and monetize it. Qasar and Peter discuss the advantages of developing their own OS in-house for their autonomous applications, self-driving technology’s potential to drive re-shoring of vehicle manufacturing to the United States, and how best to gauge the bar for safety in autonomous systems. Plus, they explore how self-driving technology may reshape the designs of not only vehicles, but cities themselves. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @qasar | @AppliedInt Chapters: 00:00 Qasar Younis and Peter Ludwig Introduction 01:28 A Primer on Applied Intuition 11:08 Applied Intuition’s Customers 12:04 Impact of Chinese Vehicles Manufacturers 15:44 EV Policies in the European Market 20:49 Can Robotics and Automation Re-Shore Vehicle Manufacturing? 21:53 Training Models for Autonomous Vehicles 26:41 Gauging the Bar for Autonomous Vehicles Safety 32:03 Timeline for Large-Scale Autonomous Vehicle Adoption 36:28 Rethinking Urban Design for Autonomous Vehicles 38:47 How Applied Intuition Uses AI for Tooling and OS 42:09 Designing for User Experience 43:31 Applied Intuition’s Hiring Strategy 45:01 Conclusion

What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @8enmann Links:  ai-2027.com/  Chapters: 00:00 Ben Mann Introduction 00:33 Releasing Claude 4 02:05 Claude 4 Highlights and Improvements 03:42 Advanced Use Cases and Capabilities 06:42 Specialization and Future of AI Models 09:35 Anthropic's Approach to Model Development 18:08 Human Feedback and AI Self-Improvement 19:15 Principles and Correctness in Model Training 20:58 Challenges in Measuring Correctness 21:42 Human Feedback and Preference Models 23:38 Empiricism and Real-World Applications 27:02 AI Safety and Ethical Considerations 28:13 AI Alignment and High-Risk Research 30:01 Responsible Scaling and Safety Policies 35:08 Future of AI and Emerging Behaviors 38:35 Model Context Protocol (MCP) and Industry Standards 41:00 Conclusion

In this episode of No Priors, Sarah and Elad are joined by Dr. Fei-Fei Li, AI pioneer, co-director of Stanford’s Human-Centered AI Institute, and founder of World Labs. Fei-Fei shares why she’s building at the intersection of embodiment and intelligence, and what today’s AI systems are still missing. From the early days of ImageNet to her vision for the next generation of robotics, she unpacks the human and technical motivations behind World Labs. They also discuss the challenges of 3D world modeling, her approach to building exceptional teams, and the special qualities that have led her students like Andrej Karpathy to make major breakthroughs. Show Notes: 0:00 Why and what Dr. Fei-Fei Li is building 3:00 World models at World Labs 6:44 Missing gaps in the AI future 9:16 Robotics and physical intelligence 16:15 Greatest challenges of 3D 19:08 Fei-Fei’s work in PhD in ImageNet 23:05 Special moments in Dr. Li's career 29:33 Building teams 32:05 Human-centered AI

In this episode of No Priors, Sarah and Elad unpack the current state of the AI market - whether it’s consolidating, what’s enabling or blocking key mergers, and where the most promising untapped opportunities lie, particularly in biotech. They also explore the rise of world models and how AI’s novel methods for understanding complex systems may ultimately reshape how humans approach discovery and problem-solving.  Show Notes: 0:00 Is the AI market consolidating into clear winners? + the physics of the current landscape 7:01 Why more companies don’t merge (even when it makes sense)  10:09 Exploring biotech’s biggest commercial opportunities and the challenges founders face  17:14 Building world models  21:34 How AI is expanding the way humans reason, design, and evolve systems

Arvind Jain joins Sarah and Elad on this episode of No Priors. Arvind is the founder and CEO of Glean, an AI-powered enterprise search platform. He previously co-founded Rubrik and spent over a decade as an engineering leader at Google. In this episode, Arvind shares how LLMs are transforming enterprise search, why most tools in the space have failed, and the opportunity to build apps powered by internal knowledge. He discusses how much customization is still needed on top of foundation models, what made building Glean uniquely challenging compared to Arvind’s previous ventures, and what’s next for the company. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jainarvind Show Notes: 0:00 Introduction 0:58 How LLMs are changing search 2:05 Building out Glean’s platform 5:09 Why most search companies failed 8:41 Out of the box vs. bespoke models  10:26 Creating apps on top of internal knowledge 15:34 User behaviors & insights  19:11 Unique challenges of building Glean  21:51 Product-led growth vs. enterprise sales 25:00 Succeeding in traditionally bad markets  27:08 What Glean is excited to build next

On this episode of No Priors, Sarah talks to Luis von Ahn, founder and CEO of Duolingo, the world’s most popular education app with over 116 million monthly users and a market cap of approximately $17 billion. Controversially, it has recently committed to being “AI-first.” They discuss why motivation is the biggest challenge in education, how Duolingo harnesses game mechanics and behavioral insights to keep learners engaged, and the company’s efforts to leverage AI to personalize education at scale. Luis also shares thoughts on the Duolingo brand, courses beyond language (chess and math), and the broader impact of AI on content creation. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LuisvonAhn Links: Duolingo is now AI-First: http://bit.ly/3RQzny3 Show Notes: 0:00 Introduction 4:01  Optimizing learning behavior through tech 11:20 Adopting AI at Duolingo 17:25 AI’s threat to content companies 18:34 An unhinged corporate brand 21:28 How do people learn? 25:16 What people misunderstand about Duolingo? 26:24 How AI is transforming learning at scale 30:28 Leveraging AI across the business

This week on No Priors, Elad and Sarah sit down with Eric Mitchell and Brandon McKinzie, two of the minds behind OpenAI’s O3 model. They discuss what makes O3 unique, including its focus on reasoning, the role of reinforcement learning, and how tool use enables more powerful interactions. The conversation explores the unification of model capabilities, what the next generation of human-AI interfaces could look like, and how models will continue to advance in the years ahead. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mckbrando | @ericmitchellai Show Notes: 0:00 What is o3? 3:21 Reinforcement learning in o3 4:44 Unification of models 8:56 Why tool use helps test time scaling 11:10 Deep research 16:00 Future ways to interact with models 22:03 General purpose vs specialized models 25:30 Simulating AI interacting with the world 29:36 How will models advance?

On this episode of No Priors, Sarah sits down with Isa Fulford, one of the masterminds behind deep research. They unpack how the initiative began, the role of human expert data, and what it takes to build agents with real-world capability and even taste. Isa shares the differences between deep research and OpenAI’s o3 model, the challenges around latency, and how she sees agent capabilities evolving. Plus, OpenAI has announced that deep research is free for all US users starting today. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @IsaFulf Show Notes: 0:00 Deep research’s inception & evolution 6:12 Data creation  7:20 Reinforcement fine-tuning 9:05 Why human expert data matters 11:23 Failure modes of agents 13:55 The roadmap ahead for Deep Research 18:32 How do agents develop taste?  19:29 Experience and path to building a broadly capable agent 22:03 Deep research vs. o3 25:55 Latency 27:56 Predictions for agent capabilities

This week on No Priors, Sarah and Elad are joined by Josh Goldman, cofounder and president of KoBold Metals. KoBold is using AI to transform how we discover critical minerals like lithium and cobalt, making the exploration process faster, more precise, and more scalable than traditional methods. In this episode, Josh explains how KoBold is rethinking the fundamentals of mineral exploration by combining unique datasets, scientific modeling, and predictive algorithms. They dive into the company’s driving philosophy and technical approach, how they validate underground hypotheses, and why regulatory knowledge and a localized approach are crucial. Josh also discusses what success looks like in exploration today and the scarcity of world-class deposits. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @KoBold_Metals Show Notes: 0:00 Introduction 0:29 KoBold Metals 3:14 Using unique datasets 6:20 Traditional methods of lithium exploration 8:38 Regulatory vs. rarity constraints 13:40 Technical approach 16:25 Validating hypotheses 23:56 Redefining success in mineral exploration 25:44 Scarcity of good projects and deposits 32:44 Philosophy behind prediction 36:46 KoBold’s origin story

On this episode of No Priors, Sarah and Elad sit down with Brendan Foody, CEO and cofounder of Mercor, to discuss the company’s rapid growth and their vision for the future of the labor market. They dive into how AI is reshaping the workforce in real, tangible ways and what skills are worth investing in today. Brendan shares insights on evaluating talent in an AI-driven world, including how models might identify outlier or 10x candidates and even assess “taste.” The conversation also touches on the evolving role of human data, the future of hiring in fast-scaling startups, and whether AI will act as an individual contributor or a data-centric manager. Show Notes: 0:00 Introduction 0:16 Building Mercor 3:00 Identifying outlier talent with AI 9:07 How AI is reshaping the workforce: job displacement & evolution 11:18 What skills should we invest in now? 12:18 Verifiability 13:36 Evaluating models 16:07 What should kids learn today? 17:05 Evaluating taste in talent assessments 18:45 Future of data collection 26:07 Humans’ role in the AI economy 28:53 AI as a contributor vs. a manager 33:03 Mercor’s goals  34:50 Evolution of labor markets  36:00 Hiring advice

In this episode of No Priors, Sarah and Elad examine the current state of AI. They break down the recent dip in public markets, how tariffs could impact the tech industry, and where opportunities remain in large language models. They highlight the opportunities in more specialized models, new approaches to model development, and how the market is beginning to standardize with integrations like the Model Context Protocol (MCP). The episode ends with a look at early consumer AI applications and what types of expertise will matter most in the coming years. Show Notes: 0:00 Improvements in image gen 4:42 Public markets  8:08 Effects of tariffs on tech 9:42 Today’s large model market 11:34 Opportunities in specialized models 16:30 Research advances in model approaches 21:10 What expertise will matter? 24:30 Anthropic’s Model Context Protocol  26:30 Consumer applications

In this episode of No Priors, Elad and Sarah chat with Shiv Rao, MD, founder and CEO of Abridge. They dive into how Abridge is reshaping healthcare by creating AI tools that enhance clinical documentation and improve doctor-patient interactions. Shiv shares his thoughts on building trust with established healthcare systems, giving agency and time back to clinicians, and what makes the healthcare AI opportunity different today. They also discuss Abridge’s approach to developing and launching AI products, along with Shiv’s journey in founding Abridge. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ShivdevRao Show Notes:  0:00 Introduction  0:35 Abridge’s Story and Vision  5:30 Strategy for Customer Choice  7:41 Healthcare AI Opportunities  11:24 Navigating Incumbent Partnerships  14:26 Doctor-Centric AI Solutions  19:54 Abridge’s Future Plans  22:13 AI’s Impact on Healthcare  28:43 Shipping and Iterating Products  32:50 Shiv’s Journey to Abridge

This week on No Priors, Elad speaks with Chelsea Finn, cofounder of Physical Intelligence and currently Associate Professor at Stanford, leading the Intelligence through Learning and Interaction Lab. They dive into how robots learn, the challenges of training AI models for the physical world, and the importance of diverse data in reaching generalizable intelligence. Chelsea explains the evolving landscape of open-source vs. closed-source robotics and where AI models are likely to have the biggest impact first. They also compare the development of robotics to self-driving cars, explore the future of humanoid and non-humanoid robots, and discuss what’s still missing for AI to function effectively in the real world. If you’re curious about the next phase of AI beyond the digital space, this episode is a must-listen. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ChelseaFinn Show Notes: 0:00 Introduction 0:31 Chelsea’s background in robotics 3:10 Physical Intelligence  5:13 Defining their approach and model architecture 7:39 Reaching generalizability and diversifying robot data 9:46 Open source vs. closed source 12:32 Where will PI’s models integrate first? 14:34 Humanoid as a form factor 16:28 Embodied intelligence 17:36 Key turning points in robotics progress 20:05 Hierarchical interactive robot and decision-making 22:21 Choosing data inputs 26:25 Self driving vs robotics market 28:37 Advice to robotics founders 29:24 Observational data and data generation 31:57 Future robotic forms

This week on No Priors, Sarah and Elad talk with GitHub CEO Thomas Dohmke about the rise of AI-powered software development and the success of Copilot. They discuss how Copilot is reshaping the developer workflow, GitHub’s new Agent Mode, and competition in the developer tooling market. They also explore how AI-driven coding impacts software pricing, the future of open source vs. proprietary APIs, and what Copilot’s success means for Microsoft. Plus, Thomas shares insights from his journey growing up in East Berlin and navigating rapidly changing worlds. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ThomasDohmke Show Notes: 0:00 Introduction 0:37 GitHub Copilot’s capabilities 4:12 Will agents replace developers? 6:04 Copilot’s development cycle 8:34 Winning the developer market 10:40 Agent mode 13:25 Where GitHub is headed 16:45 Building for the new challenges of AI 21:50 Dev tools market formation 29:56 Copilot’s broader impact 32:17 How AI changes software pricing 39:16 Open source vs. proprietary APIs 48:01 Growing up in East Berlin

This week on No Priors, Sarah is joined by Dan Hendrycks, director of the Center of AI Safety. Dan serves as an advisor to xAI and Scale AI. He is a longtime AI researcher, publisher of interesting AI evals such as "Humanity's Last Exam," and co-author of a new paper on National Security "Superintelligence Strategy" along with Scale founder-CEO Alex Wang and former Google CEO Eric Schmidt. They explore AI safety, geopolitical implications, the potential weaponization of AI, along with policy recommendations. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @DanHendrycks Show Notes: 0:00 Introduction 0:36 Dan’s path to focusing on AI Safety 1:25 Safety efforts in large labs 3:12 Distinguishing alignment and safety 4:48 AI’s impact on national security 9:59 How might AI be weaponized? 14:43 Immigration policies for AI talent 17:50 Mutually assured AI malfunction 22:54 Policy suggestions for current administration 25:34 Compute security 30:37 Current state of evals

This week on No Priors, Sarah sits down with Noubar Afeyan, Co-founder and CEO of Flagship Pioneering, the biotech firm behind groundbreaking companies like Moderna. They explore how Flagship creates the conditions for scientific breakthroughs, tackles regulatory uncertainty, and pushes the boundaries of discovery. Noubar shares insights on AI’s role in healthcare, the challenges of bringing new therapies to market, and lessons learned from past pandemics. He also discusses Flagship’s platform approach to biotech innovation and introduces the idea of polyintelligence. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @NoubarAfeyan Show Notes:  0:00 Introduction 0:48 Founding Flagship  5:51 Fostering environments for emergence 11:17 Expanding into new frontiers 14:26 Developing technology amid regulatory uncertainty and risk 19:12 How Flagship has evolved 22:47 AI applications in healthcare 27:30 Bottlenecks in bringing new therapies to market 32:20 Lessons for the next pandemic 34:11 Building a platform 38:10 Polyintelligence

On this week’s episode of No Priors, Sarah Guo is joined by leading members of the teams at Vevo Therapeutics and the Arc Institute – Nima Alidoust, CEO/Co-Founder at Vevo Therapeutics; Johnny Yu, CSO/Co-Founder at Vevo Therapeutics; Patrick Hsu, CEO/Co-Founder at Arc Institute; Dave Burke, CTO at Arc Institute; and Hani Goodarzi, Core Investigator at Arc Institute. Predicting protein structure (AlphaFold 3, Chai-1, Evo 2) was a big AI/biology breakthrough. The next big leap is modeling entire human cells—how they behave in disease, or how they respond to new therapeutics. The same way LLMs needed enormous text corpora to become truly powerful, Virtual Cell Models need massive, high-quality cellular datasets to train on. In this episode, the teams discuss the groundbreaking release of the Tahoe-100M single cell dataset, Arc Atlas, and how these advancements could transform drug discovery. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @Nalidoust | @IAmJohnnyYu | @PDHsh | @Davey_Burke | @Genophoria Download the Tahoe Dataset Show Notes: 0:00 Introduction 1:40 Significance of Tahoe-100M dataset 4:22 Where we are with virtual cell models and protein language models 10:26 Significance of perturbational data 17:39 Challenges and innovations in data collection 24:42 Open sourcing and community collaboration 33:51 Predictive ability and importance of virtual cell models 35:27 Drug discovery and virtual cell models 44:27 Platform vs. single hypothesis companies 46:05 Rise of Chinese biotechs 51:36 AI in drug discovery