The traditional call center may soon be a thing of the past. Jessie Zhang is building AI agents designed to replace monotonous human labor and transform how consumers interact with brands. Elad Gil sits down with Jesse Zhang, co-founder and CEO of Decagon, an AI agent company at the forefront of AI customer service. Jesse talks about how Decagon secured large enterprise clients and the impact of its AI agents, his journey as a second-time founder, and Decagon’s company culture. Plus, they discuss what the future of agentic customer service may look like. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thejessezhang | @DecagonAI  Chapters: 00:00 – Jesse Zhang Introduction 00:30 – Decagon’s Services 01:11 – Decagon’s Customers and Growth 02:41 – Productivity Gains with Decagon 03:33 – How Decagon Integrates in Customer Workflows 04:25 – Jesse’s Second Time Founder Story 05:41 – Jesse’s Hiring Philosophy 09:13 – Counter-intuitive Advice for Founders 11:19 – How Decagon Thinks About Talent 14:12 – Areas for Longer Term Planning 15:37 – Decagon’s Path to Customer Service 16:57 – Thoughts on Pushing Into the Application Layer 19:40 – What Decagon Does Uniquely  22:05 – Pricing Services in the AI Age 24:46 – How Decagon Sees Customer Service 25:53 – Defining Long-Term Success for Decagon 27:41 – Jesse’s Views on an Agentic Future 31:22 – Conclusion

From negotiating with world leaders to partnering with top entrepreneurs, businessman and investor Jared Kushner has traveled the unique path of bringing private sector knowledge to government work and back again. Jared Kushner joins Sarah Guo and Elad Gil to cover a wide range of topics, from his founding of investment firm Affinity Partners, to his time in government, to his new AI venture BrainCo. Jared discusses Affinity Partners’ mission and strategy, how he has leveraged his government experience in business and investing, and the geopolitics of technological advancements like AI. Plus, he makes a case for why private sector talent should do “tours of duty” in government. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jaredkushner Chapters: 00:00 – Jared Kushner Introduction 00:30 – Starting Affinity Partners Post-Government 01:59 – Value of Global Perspective 03:34 – Ventures with Affinity 05:14 – Evaluating Investments Via Macro Trends 09:09 – Undervalued Countries 12:32 – Origins of BrainCo 16:50 – BrainCo Use Cases 23:49 – BrainCo’s Biggest Challenge 24:47 – Determining Customer Fit 26:39 – AI and Policy 30:03 – Middle East and AI 31:59 – Jared’s Experience in Middle East Diplomacy 40:16 – Brokering Peace Post-October 7th 43:52 – Making Deals with Middle Eastern Partners 47:14 – Jared and Ivanka’s Partnership 49:18 – Benefits of Joining Public Sector from the Private Sector 52:07 – Jared’s Pitch for Serving in Government 56:25 – Jared’s Leadership Style 58:24 – Conclusion

How does a new technology get adopted by 40% of American doctors in just 18 months? In an era where the golden age of biotechnology has also created a dark age of physician burnout, OpenEvidence found the answer by fundamentally changing how doctors access critical information. OpenEvidence founder Daniel Nadler sits down with Sarah Guo and Elad Gil to discuss how his company solved the semantic search problem in medicine. He talks about the strategy of treating doctors as consumers, striking the balance of keeping patients in the loop in medical conversations, and how technology will reshape both medicine and medical education. Plus, Daniel gives his thoughts on the roots of motivation, as well as his philosophy for recruitment.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EvidenceOpen  Chapters: 00:00 – Daniel Nadler Introduction 00:08 – OpenEvidence’s Success  01:54 – How OpenEvidence Works 06:35 – Dealing with Ambiguity 11:37 – Treating Knowledge Workers as Consumers 15:53 – Balancing Keeping Patients in the Loop 19:28 – How Technology May Shape the Future of Medicine 22:12 – How Technology Will Change Medical Education 30:40 – Examining Consumer Adoption of Preventative Health Measures 36:02 – Lessons for Other Fields 37:27 – Rationalism vs. Will 41:13 – Daniel’s Thoughts on Motivation 42:44 – Daniel’s Recruiting Philosophy 44:48 – Conclusion

AI doomers say that the technology will be the ultimate job-killer. But Jacob Helberg wants people to see AI as a tech that will boost, not replace, human workers and give them superpowers. Under Secretary of State for Economic Growth, Energy, and the Environment Jacob Helberg joins Sarah Guo and Elad Gil to talk about AI’s role in reshoring manufacturing in America, supply chain security, and transforming the US energy grid. He also discusses the CapEx revolution, why he sees opportunity for tech and energy partnerships in the Middle East, and the path to more nuclear energy for the US. Plus, the three explore what the “superintelligence century” could look like. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jacobhelberg  Chapters: 00:00 – Jacob Helberg Introduction 00:50 – Jacob’s Agenda for Capitol Hill 01:53 – Reshoring the American Supply Chain 04:38 – Areas of CapEx Growth 06:56 – Importance of Supply Chain Security 08:52 – Reshoring Rare Earth Minerals 11:12 – How AI Can Help America Reindustrialize  15:37 – AI and Productivity Gains 17:38 – The Superintelligence Century 22:56 – Creating an Open Source AI Ecosystem 24:41 – The Middle East and AI 26:24 – Growing Energy Resources in the US 28:28 – The Path to More Nuclear Energy in the US 35:50 – Essential Domains for Strategy and Security 38:20 – The Tech Industry and the Administration 40:29 – Conclusion

Andrew Ng has always been at the bleeding edge of fast-evolving AI technologies, founding companies and projects like Google Brain, AI Fund, and DeepLearning.AI. So he knows better than anyone that founders who operate the same way in 2025 as they did in 2022 are doing it wrong. Sarah Guo and Elad Gil sit down with Andrew Ng, the godfather of the AI revolution, to discuss the rise of agentic AI, and how the technology has changed everything from what makes a successful founder to the value of small teams. They talk about where future capability growth may come from, the potential for models to bootstrap themselves, and why Andrew doesn’t like the term “vibe coding.” Also, Andrew makes the case for why everybody in an organization—not just the engineers—should learn to code.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AndrewYNg Chapters: 00:00 – Andrew Ng Introduction 00:32 – The Next Frontier for Capability Growth 01:29 – Andrew’s Definition of Agentic AI 02:44 – Obstacles to Building True Agents 06:09 – The Bleeding Edge of Agentic AI 08:12 – Will Models Bootstrap Themselves? 09:05 – Vibe Coding vs. AI Assisted Coding 09:56 – Is Vibe Coding Changing the Nature of Startups? 11:35 – Speeding Up Project Management 12:55 – The Evolution of the Successful Founder Profile 19:23 – Finding Great Product People 21:14 – Building for One User Profile vs. Many 22:47 – Requisites for Leaders and Teams in the AI Age 28:21 – The Value of Keeping Teams Small 32:13 – The Next Industry Transformations 34:04 – Future of Automation in Investing Firms and Incubators 37:39 – Technical People as First Time Founders 41:08– Broad Impact of AI Over the Next 5 Years 41:49 – Conclusion

What would it take to challenge Nvidia? SemiAnalysis Founder and CEO Dylan Patel joins Sarah Guo to answer this and other topical questions around the current state of AI infrastructure. Together, they explore why Dylan loves Android products, predictions around OpenAI’s open source model, and what the landscape of neoclouds looks like. They also discuss Dylan’s thoughts on bottlenecks for expanding AI infrastructure and exporting American AI technologies. Plus, we find out what question Dylan would ask Mark Zuckerberg.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @dylan522p | @SemiAnalysis_ Chapters: 00:00 – Dylan Patel Introduction 00:31 – Dylan’s Love for Android Products 02:10 – Predictions About OpenAI’s Open Source Model 06:50 – Implications of an American Open Source Model for the Application Ecosystem 10:48 – Evolution of Neoclouds 17:26 – What It Would Take to Challenge Nvidia 27:43 – What Would an Nvidia Challenger Look Like? 28:18 – Understanding Operational and Power Constraints for Data Centers 34:48 – Dylan’s View on the American Stack 43:01 – What Dylan Would Ask Mark Zuckerberg 44:22 – Poker and AI Entrepreneurship 46:51 – Conclusion

Cloudflare has spent nearly fifteen years making the Internet faster, more reliable, and more secure. So now that AI systems are changing the way we interact with the Internet, Cloudflare wants to help level the playing field for content creators. Sarah Guo and Elad Gil sit down with Matthew Prince, co-founder and CEO of Cloudflare to discuss the evolution of the internet from search to AI, including Cloudflare’s role in facilitating that shift. Matthew talks about how AI assistants are changing the shape of the Internet, the problems Google created by making traffic the arbiter of content value, and how he sees Cloudflare’s part in facilitating the new content marketplace for the mutual benefit of creators and AI companies. Plus, a look towards how agentic infrastructure may unfold in the near future. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @eastdakota | @Cloudflare Chapters: 00:00 – Matthew Prince Introduction 00:37 – Cloudflare’s Role in Securing the Internet 02:08 – The Road to Cloudflare’s Dominance 03:20 – The Internet’s Shift from Search to AI 06:34 – Role of Agents and Content on the New Web 09:44 – Reshaping the Content Market Online 13:05 – De-emphasizing Traffic as a Proxy for Value 18:04 – Will We Run Out of Quality Human-Generated Content? 20:01 – Scaling the Value of Content in the AI Age 22:32 – Cloudflare’s Approach to Inference 24:55 – How Cloudflare Responds to Market Demand 26:04 – Open vs. Closed Models 27:21 – Path to the New Marketplace for Content 30:58 – Advice for Content Creators 32:47 – Exploring the Timeline for Running Models Locally 40:07 – The Future of Agentic Infrastructure 44:52 – Conclusion

Sriram Krishnan was never interested in policy. But after seeing a gap in AI knowledge at senior levels of government, he decided to lend his expertise to the tech-friendly Trump administration. Senior White House Policy Advisor on AI Sriram Krishnan joins Elad Gil and Sarah Guo to talk about America’s AI Action Plan, a recent executive order that outlines how America can win the AI race and maintain its AI supremacy. Sriram discusses why winning the AI race is important and what that looks like, as well as the core goals of the Action Plan that he helped to author. Together, they explore how AI is the latest iteration of American cultural exportation and soft power, the bottlenecks in upgrading America’s energy infrastructure, and the importance of America owning the “full stack” from GPUs and models to agents and software. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @skrishnan47 | @sriramk Chapters: 00:00 – Sriram Krishnan Introduction 01:00 – Sriram’s Role in Government 03:43 – Impetus for the America AI Action Plan 06:14 – What Winning the AI Race Looks Like 10:36 – Algorithms and Cultural Bias 12:26 – Main Tenets of the America AI Action Plan 19:13 – Infrastructure and Energy Needs for AI 22:56 – Manufacturing, Supply Chains, and AI 24:52 – Ensuring American Dominance in Robotics 26:30 – Translating Policy to Industry and the Economy 29:30 – Should the US Be a Technocracy? 32:33 – Understanding the Argument Against Open Source Models 36:07 – Conclusion

In the generative AI revolution, quality data is a valuable commodity. But not all data is created equally. Sarah Guo and Elad Gil sit down with SurgeAI founder and CEO Edwin Chen to discuss the meaning and importance of quality human data. Edwin talks about why he bootstrapped Surge instead of raising venture funds, the importance of scalable oversight in producing quality data, and the work Surge is doing to standardize human evals. Plus, we get Edwin’s take on what Meta’s investment into Scale AI means for Surge, as well as whether or not he thinks an underdog can catch up with OpenAI, Anthropic, and other dominant industry players. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @echen | @HelloSurgeAI Chapters: 00:00 – Edwin Chen Introduction 00:41 – Overview of SurgeAI 02:28 – Why SurgeAI Bootstrapped Instead of Raising Funds 07:59 – Explaining SurgeAI’s Product 09:39 – Differentiating SurgeAI from Competitors  11:27 – Measuring the Quality of SurgeAI’s Output 12:25 – Role of Scalable Oversight at SurgeAI 14:02 – Challenges of Building Rich RL Environments 16:39 – Predicting Future Needs for Training AI Models 17:29 – Role of Humans in Data Generation 21:27 – Importance of Human Evaluation for Quality Data 22:51 – SurgeAI’s Work Toward Standardization of Human Evals 23:37 – What the Meta/ScaleAI Deal Means for SurgeAI 24:35 – Edwin’s Underdog Pick to Catch Up to Big AI Companies 24:50 – The Future Frontier Model Landscape 26:25 – Future Directions for SurgeAI 29:29 – What Does High Quality Data Mean? 32:26 – Conclusion

Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent’s memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google’s “non-acquisition” of Windsurf.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai Chapters: 00:00 – Misha Laskin Introduction 00:44 – Superintelligence vs. Super Intelligent Autonomous Systems 03:26 – Misha’s Journey from Physics to AI 07:48 – Asimov Product Release 11:52 – What Differentiates Asimov from Other Agents 16:15 – Asimov’s Eval Philosophy 21:52 – The Types of Queries Where Asimov Shines 24:35 – Designing a Team-Wide Memory for Asimov 28:38 – Leveraging Pre-Trained Models 32:47 – The Challenges of Solving Scaling in RL 37:21 – Training Agents in Copycat Software Environments 38:25 – When Will We See ASI?  44:27 – Thoughts on Windsurf’s Non-Acquisition 48:10 – Exploring Non-RL Datasets 55:12 – Tackling Problems Beyond Engineering and Coding 57:54 – Where We’re At in Deploying ASI in Different Fields 01:02:30 – Conclusion

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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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 show@no-priors.com 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?