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Founder and CEO of full-stack AI chip company Fractile, Walter Goodwin, joins Sarah Guo to discuss the bets he’s made on the future of the chip market as other major players like Broadcom, NVIDIA, and AMD try to accelerate their workloads. They discuss the difference in Fractile’s newer approach on model architecture with a full-stack team in the current chip landscape and the technical bets they’re making in that direction. Walter also talks about compressing the gap between the chip design cycle and its payoff period, and making a generational leap in AI inference to realize the bet in volume against the value to be captured.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @goodwin_ml | @fractile_ai Chapters: 00:46 – Walter Goodwin and Fractile Introduction  02:29 – The Chip Landscape Now 04:56 – Common Handoffs From Architecture-Focused Players 07:18 – Full Stack Approach and Team Setup 09:49 – Fractile’s Most Important Technical Bets  15:32 – Workload Predictions and Compressing the Chip Design Cycle 23:03 – Architect Intent to Output Bottlenecks and Accelerating Trials 28:16 – Workload Bets on Model Architectural Shifts 31:20 – The Future of AI Chip Players and Market Structure   35:14 – Conclusion

Can AI transform legacy incumbents rather than replacing them? Sequence Holdings co-founder and CEO Michael Lee joins Sarah Guo to discuss how holding company structures and engineering integrations are reshaping market leaders from the inside out. Michael details Sequence’s $7.7 billion take-private transaction of Baldwin alongside Dell Family Office (DFO), and shares his thesis on why traditional consulting models and software sales fall short for real enterprise AI transformations. They also talk about why permanent holding company structures are good for long-term compounding, real-world results from applying frontier engineering to BankSouth, and Michael’s lessons from his time in public investing, private equity, and operating at the intersection of market incumbents and AI. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mjlee_2014 | @seqholdings Chapters: 00:34 – Michael Lee Introduction 01:03 – Sequence Holdings and Baldwin 01:54 – Idea for Sequence 04:36 – Incumbents in the AI Era 06:32 – Why a Holding Company 11:21 – Recruiting Top Engineers 13:08 – Investing in BankSouth 17:46 – Why an Insurance Brokerage 20:17 – Atlas Platform Explained 23:53 – Traditional Private Equity Limitations 27:23 – What Sequence Looks For in Management Teams 31:04 – Accomplishments at BankSouth 34:45 – Founder Lessons 36:08 – Story of Dell Partnership 37:10 – Career and Investment Approach 40:00 – Value of Exceptional People 42:23 – Conclusion

As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @StefanoErmon | @_inception_ai Chapters: 00:00 – Stefano Ermon Introduction 00:35 – Research Background 02:54 – Starting Inception 05:59 – Why Diffusion Beats Autoregressive 11:10 – Discrete vs. Continuous Modalities 13:19 – Inception Today 16:45 – Where Speed Wins 17:31 – Inception Customer Base 18:49 – Interaction with Hardware Landscape 19:34 – Inception and the Broader Industry 21:41 – Data Compression and Structure 24:45 – Controllability of Diffusion Modeles 27:25 – Emergent Capabilities at Scale 29:02 – Future Workload Split Between Diffusion vs. Traditional 30:03 – Adoption Challenges 31:44 – Hiring and Team Organization 32:50 – Recursive Self Improvement 34:02 – Resource Allocation 35:10 – Impact of Academia 38:13 – Conclusion

From the tokenization of real-world assets to funding research on anti-aging therapeutics, Coinbase co-founder and CEO Brian Armstrong is focused on solving meta-problems for the benefit of society. Brian joins Elad Gil this episode to discuss the intersections of artificial intelligence, crypto rails, and biotechnology. He details Coinbase’s vision for agentic finance, as well as how Coinbase is becoming the ‘Everything Exchange’, which includes tokenized real-world stocks, stablecoin payment adoption, and prediction markets. Brian also introduces his new venture, New Limit, a company founded to tackle longevity through epigenetic reprogramming, and outlines initial therapeutic programs and the roadmap toward clinical trials.   Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @brian_armstrong | @Coinbase Chapters: 00:00 – Cold Open Trailer 00:45 – Brian Armstrong Introduction 01:13 – Coinbase and the Everything Exchange 05:52 – Agentic Commerce 10:00 – AI and Crypto 10:49 – AI Inside Coinbase 15:39 – Productivity and Company Size 17:26 – The Everything Exchange and Tokenization 20:53 – Prediction Markets 23:15 – Introducing New Limit 34:17 – Looking Forward Five Years 40:24 – Special Economic Zones 44:26 – Conclusion

From data center orchestrators to AGI and robotics, CPUs remain the heart of modern computing. Arm CEO Rene Haas joins Elad Gil and Sarah Guo to explore how Arm is positioned at the epicenter of AI-driven demands for compute. Rene explains Arm’s position in the chip supply chain, and how Arm transitioned from an IP licensing model to producing physical chips like the Arm AGI CPU for Meta. He also discusses bottlenecks in hardware supply chains, SoftBank’s ecosystem and capital strategy, why US semiconductor manufacturing independence is critical, the future of robotics, and why CPUs remain crucial for executing AI workloads. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @renehaas237 | @Arm Chapters: 00:00 – Cold Open Trailer 00:49 – Rene Haas Introduction 01:14 – Arm and Chip Supply Chain 02:37 – Shift from IP to Manufacturing CPUs 04:23 – CPU IP and Customers 06:55 – AI Adoption at Arm 10:15 – Changes in Chip Time to Market 13:27 – Data Center Buildout Bottleneck 15:13 – Softbank Leverage and Capital Strategy 17:43 – Softbank Portfolio Overview 20:13 – Robotics Opportunities for Arm 24:49 – US Manufacturing Protectionism 28:59 – Data Center Backlash 32:30 – Arm Outlook 33:31 – CPU Opportunity 37:06 – Conclusion

Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich Chapters: 00:00 – Cold Open Trailer 00:59 – Ofir Ehrlich and Gonen Stein Introduction 01:27 – What Eon Does 02:41 – Data as Moat 06:43 – Training Agents with Good Data 09:39 – Data is the New Oil  15:00 – Autonomous Security Threats 18:15 – How Agents Change the Enterprise Stack 22:11 – Re-imagining Data Infrastructure 27:52 – Cloud vs. AI Era Shift 30:26 – How AI is Changing Companies 34:31 – Conclusion

Max Hodak, co-founder and CEO of Science Corporation, joins Sarah Guo to discuss the future of vision, brain-computer interfaces, and the human experience. Max explains how Science’s PRIMA retinal implant could restore functional vision for people who have lost their sight, and why treating the brain as a computational system could unlock new approaches to medicine. They explore the broader potential of neural devices, from restoring lost capabilities to expanding human potential, as well as deeper questions around identity, consciousness, and whether the human experience can persist as our biological hardware changes. Max also shares Science’s long-term vision for reducing the fragility of the human condition by repairing, replacing, and ultimately upgrading parts of ourselves. Finally, he discusses the surprising parallels between AI models and biological brains, and why AI may offer a powerful new lens for understanding intelligence. Chapters: 00:00 – Cold Open Trailer 01:40 – Max Hodak Introduction 02:00 – Science Corporation Overview and Origin 02:53 – A Revolutionary Solve for Blindness  06:32 – Scope of Timeline and Engineer Cost 09:10 – Clinic Trial Process 09:45 - The Response from Clinicians 12:21 – Broader Biotech Landscape 14:59 – Brain’s Relationship to Senses 17:35 – The Study of Consciousness  19:50 – Investments in Brain Computer Interface 22:10 – Fertile Ways to Study Neuroscience 24:46 – Biotech Expansion for Science Corporation 27:50 – What Success Looks Like in Neuroscience and Tech 29:06 - Goals Within Human Preservation vs. Adaptation  30:25 – Conclusion

In a world of infinite gaming and entertainment possibilities, how does a centuries-old game stay so popular? Chess.com co-founder and CEO Erik Allebest joins Sarah Guo to explain how the evolution of technology has kept people coming back to chess, even when machines can beat us at the game. Erik talks about how the desire to build a MySpace-like community for chess led to the purchase of a domain name from a bankruptcy sale back in 2005, and scaled into a community with 10 million daily active users and 250 million total registered members. He also discusses the growth of the cultural relevance of chess, how investments from private equity firms General Atlantic and CVC helped grow and strengthen their platform, and how Chess.com is leveraging AI both within the business itself and to make a better product for its community. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @chesscom | @erikallebest Chapters: 00:00 – Cold Open Trailer 01:05 – Erik Allebest Introduction 01:48 – Chess.com Today 02:57 – Buying and Scaling Chess.com  06:29 – Competition and Growth 11:52 – Chess and Cultural Relevance 14:32 – Private Equity Investment 19:31 – Playing Games Amid Evolving Tech 25:09 – Tech, Skill Distribution, and Expertise 28:40 – Chess and Cheating 31:20 – What Makes Chess Special 33:17 – Chess.com Future Vision 34:54 – Founder Advice 36:48 – AGI/ASI Predictions 40:02 – AI Investments at Chess.com  42:13 – How AI May Change Product at Chess.com 43:27 – Poker Rating Algorithms 46:07 – Conclusion

Is the tech industry moving too quickly, or are founders letting fear of AI labs stunt their ambitions? Sarah and Elad explore the current landscape of artificial intelligence, venture capital, and startup dynamics. They discuss the realities of building multi-trillion-dollar companies, shifting market sizes and outcome-based pricing models, and how founders are reacting to the rise of major AI labs. They also talk about what the framework for startup exits should look like, the potential for researcher burnouts in the next eighteen months as ASI looms on the horizon, bottlenecks for compute, and the impact of regulatory capture and shifting ecosystems from California to Texas. Apply for Embed - Conviction’s Catalyst for AI-Native Startups Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Cold Open Trailer 00:31 – Episode Introduction 01:44 – The Next Trillion-Dollar Company 03:12 – Tech Waves as Punctuated Equilibria  04:42 – TAM vs. Revenue Reality 07:14 – Market Size vs. Speed 10:32 – When Founders Should Sell 14:04 – Financing and Time Cost 17:57 – RSI and the Looming Promise of ASI 21:49 – Compute Power Laws 28:12 – Regulations and Disruption 33:06 – Beyond Transformers 34:26 – Tradeoffs - Safety vs. Progress 39:11 – Conclusion

When your AC fails in a heatwave, you don’t want a busy signal; you need a solution. Netic founder and CEO Melisa Tokmak joins host Elad Gil to explain how Netic’s autonomous AI platform acts as an intermediary between companies and customers, deploying agents to instantly handle essential services, from emergency home repairs to hospitality to pet care. Melisa describes the complexity of these real-world workloads, which have traditionally relied on large human support teams, and how over 70% of Netic’s customers interact first with AI. She also talks about the reasoning behind building a scalable product company rather than an AI roll-up, why she believes robotics will not catch up in these industries in the near future, why she doesn’t view large frontier labs as competitive threats, and how private equity’s playbook has shifted toward measurable ROI in the AI-era. Plus, why Melisa is optimistic about the impact AI will have on education. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @netic_AI | @melisatokmak Chapters: 00:00 – Melisa Tokmak Introduction 00:32 – What Netic Builds 03:53 – Automating Workflows for Essential Services 06:26 – Building a Service vs. AI Roll-Up 10:38 – AI for the Real World Timeline 12:56 – Can Big Labs Compete? 15:35 – Modern Founder Mindset 19:09 – Screening for Agency 22:25 – Five Year Vision 23:53 – Selling to Slow Industries 27:23 – How Private Equity Approached AI 31:14 – What Excites Melisa About the Future of AI 34:27 – Conclusion

DoorDash is not just a delivery company. From its inception, co-founders Andy Fang and Stanley Tang operated it as a robotics and autonomy company. Andy and Stanley join Sarah Guo to explain how autonomous tech and AI are reshaping consumer habits, commerce, and delivery. Andy and Stanley talk about the rollout of Ask DoorDash, a natural-language interface that’s driving both restaurant discovery and larger grocery orders. They also discuss Dot, their in-house autonomous delivery robot that has operated in Phoenix for over two years, and how it highlights the operational and hardware challenges they have faced and solved in autonomous tech. Andy and Stanley also speak about the “first and last 100 feet problem” in autonomous delivery, why multimodal strategies are the key to success, scaling autonomy and operations, and why they believe that more Dashers, not fewer, are the future of DoorDash.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @stanleytang | @andyfang | @DoorDash Chapters: 00:00 – Andy Fang and Stanley Tang Introduction 00:34 – Agentic Commerce and Behavioral Changes 03:52 – Next Steps for Ask DoorDash 06:54 – Investing in Robotics and Autonomy 16:31 – Building Autonomous Tech in the Physical World 21:20 – Dot: DoorDash’s Autonomous Delivery Robot 22:08 – Collecting Realistic Data 25:48 – Why Work at DoorDash 28:04 – Challenges in Scaling Up Autonomy 39:30 – Productivity Benchmarks 44:56 – Future of Agentic Commerce 49:10 – Conclusion

When Glenn Fogel joined Priceline in 2000, the business was worth a few hundred million dollars. One week later, the Nasdaq peaked, eventually sending its stock down to a dollar a share. But over 25 years later, Booking Holdings has scaled over 1000x into an over $100 billion dollar global travel behemoth. Elad Gil is joined by Booking Holdings CEO Glenn Fogel to discuss his career, from law school and Wall Street to working at Priceline through the dot-com crash, and to helping grow the business into a multifaceted, dynamic travel marketplace in the AI era. Glenn explains how leveraging AI and agents such as Priceline’s ‘Penny’ makes travel planning and customer service better, while emphasizing the importance of preserving some human support for some users. He also talks about Booking’s strategy of reinvesting over $700 million into AI and other technologies while still offering stock buybacks and dividends, the durability of their scale and complexities of dealing with a large portfolio physical properties across the world, and why upskilling is so important for employees amid concerns about AI-driven job displacement.      Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @bookingcom | @priceline Chapters: 00:00 – Cold Open 00:05 – Glenn Fogel Introduction 00:41 – Glenn’s Early Career 06:49 – Lessons from the Early Internet 09:24 – Deciding Factors for Exiting 10:56 – Travel Through the Lens of AI 13:30 – Agentic Travel Planning  18:59 – Agents, Token Economics, and ROI 22:46 – Booking’s Capital Investment Philosophy 25:23 – Scale as Durable Asset 29:40 – Purpose and Choosing Wisely 33:18 – AI’s Impact on Jobs 36:38 – Upskilling in the AI Era 38:36 – Public Perception of AI 40:24 – Conclusion

While the rest of the nuclear industry still relies on simulations and paper designs, Valar Atomics is busy splitting atoms. In fact, they just powered an NVIDIA Blackwell chip directly with a live nuclear reactor in order to power the world’s first nuclear powered website. Sarah Guo joins Valar Atomics founder and CEO Isaiah Taylor on-site at their reactor site in Utah to talk about how Valar is shifting nuclear energy from the theoretical to the practical by building and perfecting reactors via hardware iteration. Isaiah discusses why the US stopped building nuclear reactors in the 1970s, and how Valar utilized a little-known pathway via the Department of Energy, revived by a Trump administration executive order, to successfully develop and run their advanced reactor. He also shares Valar’s strategy for vertical integration, their venture-backed approach to financing, their giga-site plans, and why he believes cheap, abundant atomic energy has the power to vastly improve the quality of human life. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @isaiah_p_taylor | @valaratomics Chapters: 00:00 – Cold Open 00:57 – Isaiah Taylor Introduction 01:30 - Valar’s Mission and Origin 04:24 - Why Nuclear Development Stalled 07:18 - Reviving Nuclear through DoE and Executive Order 10:59 - Control Room Tour 16:17 - Misunderstandings About Nuclear 20:07 - Issues with Reliability 22:14 - Nuclear is a Hardware Execution Problem 24:32 - Timeline to Scale Production 26:32 - Introducing Ward 250 30:42 - Speed Through Simplicity 33:33 - AI Drives Nuclear Demand 35:02 - Running a Reactor with NVIDIA Blackwell 36:27 - Valar’s Nuclear Conviction 40:16 - Verticalization as Path to Scale 43:58 - Valar’s Control Skid 48:00 - Venture-Backed Nuclear 50:51 - Gigasite Strategy 53:11 - CEO Tick Rate 55:37 - Abundant Energy and Hyper-Techno Industrialism 1:01:27 – Conclusion

When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing. Read more: Implications of Large-Scale Test-Time Compute Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @polynoamial | @OpenAI Chapters: 00:00 – Cold Open 00:43 – Noam Brown Introduction 01:23 – Why Benchmarks Are Broken 04:19 – Compute Budgets and Projections 05:34 – How Long Should Models Think? 06:47 – Benchmark-Maxxing 08:34 – Using Poker Bots as Evals 11:26 – Safety Evals When Model Capability Scales With Budget  14:41 – Release Cycle vs. Agent Runtime  17:06 – Latent Model Capability  20:59 – Limits on Recursive Self-Improvement 27:09 – Large-Scale Multi-Agent Coordination  29:11 – Competition at the Frontier  31:51 – Breaking the Benchmark Grid Equilibrium  33:29 – Why Benchmarks Should be Evaluated by Cost 36:18 – Conclusion

At 66 years old, instead of heading towards retirement, former Cadence CEO and legendary investor Lip-Bu Tan decided to take on the hardest job in tech: turning Intel around. Elad Gil and Sarah Guo sit down with Intel CEO Lip-Bu Tan to talk about why he took the job and what “saving” Intel actually looks like. Tan explains how his experience in startup culture informed his decisions to drive Intel’s culture towards faster decisions, focus on customer satisfaction, and engineer accountability. He also discusses his strategy to strengthen Intel’s balance sheet by welcoming investments from Jensen Huang’s Nvidia, Softbank, and the US government. Tan also shares his product roadmap that centers the CPU for agentic AI and inference, the collaboration with Elon Musk on Terafab, his investing framework for semiconductors, and his views on how AI is reshaping design and operations at, as he puts it, a ‘legacy spreadsheet’ tech company.         Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LipBuTan1 | @intel Chapters: 00:00 – Cold Open 01:01 – Lip-Bu Tan Introduction 01:24 – Why Lip-Bu Took the Reins at Intel 03:00 – Fixing Culture 04:08 – Intel’s 10-Year Vision 07:57 – Working with Elon Musk on Terafab 09:59 – Shifting Supply Chain for Semiconductors 15:34 – Limits to Scaling and Packaging 18:30 – Physical Limits to Engineering and Design 20:33 – Challenges in Semiconductor Investing 26:29 – Lessons from Cadence 28:02 – Scaling and Investment Decisions 32:03 – Rethinking Teams in AI Era 34:31 – Industrial Policy and Funding 37:25 – What Investors Misunderstand About Intel 41:10 – Where Compute Will Live 44:59 – Conclusion

Biohub started with an ambitious goal of curing, preventing, and managing all disease by the end of the century. A decade later, thanks to the convergence of frontier AI and biological data, that goal may have been too conservative. In this episode, Elad Gil and Sarah Guo sit down with Biohub co-founders Mark Zuckerberg and Priscilla Chan, alongside Biohub Head of Science Alex Rives. Together, they discuss Biohub’s $500 million virtual biology initiative, which integrates frontier AI with wet-lab work to build predictive world models of cells, proteins, and systems. They also talk about their newly announced open-source engine for digital protein and antibody design, ESMFold2; why Biohub is a nonprofit rather than a venture-backed startup; and how hierarchical simulations will soon allow doctors to treat patients at an individual, mechanistic level.   Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Biohub | @finkd | @alexrives | @ChanZuckerberg Chapters: 00:00 – Cold Open 01:02 - Mark Zuckerberg, Priscilla Chan, and Alex Rives Introduction 01:26 – Why Biohub and Their Mission 08:27 – Integrating Frontier AI and Frontier Biology 09:45 – Micro to Macro Biological Modeling 14:22 – Mechanistic Interpretiability  16:58 – Why Biohub is a Non-Profit 21:41 – Understanding How Biology Works 24:23 – Timeline for Curing All Diseases 26:25 – Translating Research to Patient Impact 28:04 – Launch of ESMFold2 32:13 – Tackling Off-Target Effects and Edge Cases 38:39 – Putting the Tech in Individual Hands 41:06 – Talent at Biohub 44:25 – What’s Next After ESMFold2 46:10 –  Connecting ESMFold2 to Agentic Systems 46:51 – The Virtual Cell 49:33 – Defining Success for Biohub 51:52 – Biohub Strategy Update 56:20 – Conclusion

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx Chapters: 00:00 – Satya Nadella Introduction 01:48 – Reflections from Microsoft Build 03:12 – Microsoft’s AI Training Strategy 05:48 – Complexity of Real-World Deployment of AI 07:33 – Augmenting Human Capital 09:37 – Harnesses for Enterprise 11:49 – Developer Value 15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence? 15:51 – Modern Definition of IP 17:38 – Future of Vendor vs. Enterprise Agents 21:48 – Near-Term Predictions on Model Pricing 24:02 – Durability of SaaS 25:58 – What Satya’s Building 28:18 – Future of Engineering Roles 30:54 – How Microsoft Can Be More Ambitious 34:36 – Data Centers and Community Impact 38:01 – AI’s Impact on Society 39:52 - AI and Education 42:28 – Conclusion

We are now closer than ever before to living in a world where AI agents are smart enough to run our power grids and manage water supplies. How do we keep them from going rogue? Sarah Guo sits down with Maxim Bar Kogan, founder and CEO of Onyx Securities, to explore the complexities of supervising and securing autonomous agents at the enterprise level. Maxim explains Onyx’s product as an AI control plane, which oversees the permissions and flexible contexts of agents while balancing latency, cost, and reliability. He also discusses how current controls have insufficient context to monitor agent intent, tradeoffs for gradual model rollout, the need for vendor-independent oversight, and Israel’s growing AI and security talent ecosystem. Plus, why Maxim is all-in on AGI. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @maximbarkogan  Chapters: 00:00 – Cold Open 00:45 – Maxim Bar Kogan Introduction 01:10 – AutoGPT and Betting on Agent Actions 05:17 – What Onyx Product Does 07:47 – State of Deployment in Large Enterprises 09:58 – Securing Agents 12:45 – Why Proxies Don’t Work 14:11 – Why Onyx Trains Its Own Models 18:38 – Onyx’s Talent Culture 21:24 – Mechanistic Interpretability 23:35 – How Onyx Builds Customer Trust 25:10 – Mitigating Risk at the Foundational Level 27:45 – Phased Rollout of Glasswing and Daybreak 29:11 – Large Enterprise Holdouts 30:46 – Onyx and the Larger AI Security Space 32:36 – Should Labs Address Model Trust and Governance?  36:56 – What Needs to Happen in Security 39:14 – Why Maxim is AGI-Pilled 41:15 – Conclusion

Companies in Silicon Valley from Nvidia to AMD are racing to fuel the AI revolution with postage stamp-sized AI chips. Meanwhile, a chip the size of a dinner plate just fueled a $63 billion IPO for Cerebras. Elad Gil and Sarah Guo sit down with Cerebras founder and CEO Andrew Feldman to discuss the company’s journey to making one of the largest tech go-publics in history. Andrew details the multi-year journey of pioneering wafer-scale AI computing, including surviving a brutal period of being ahead of market demand. He also explains the engineering breakthroughs that led to delivering inference speeds at 20x that of standard GPUs. Andrew then shares how a remarkable $20 billion deal with OpenAI came together in only four weeks. Plus, Andrew’s thoughts on why architecting the future of AI requires the fortitude to be a “professional David” against the Goliaths of tech. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @andrewdfeldman | @Cerebras Chapters: 00:00 – Cold Open 00:36 – Andrew Feldman Introduction 01:19 – Cerebras’ Evolution 02:48 – Wafer-Scale Bet Pays Off 06:38 – Challenges and Breakthroughs 08:37 – Crossing the Market Chasm 10:38 – Scaling Software and Hardware 12:03 – Relevance of AI-Generated Coding 13:31 – Leadership and Hiring Culture 17:16 – When to Quit vs. Persist 19:40 – Why Cerebras Went Public 22:57 – The OpenAI Deal 25:54 – Open Source and Post-Trained Workloads 27:37 – How Speed Opens Up New Business 30:33 – Conclusion

Securing AI dominance requires more than just semiconductors; it demands a complete overhaul of how the West manages everything that goes into them, from rare earth minerals to actuators. Enter: Pax Silica. Sarah Guo and Elad Gil sit down with US Under Secretary of State for Economic Affairs Jacob Helberg to discuss the launch and expansion of Pax Silica, a 14-country economic security coalition designed to secure the entire AI supply chain. Jacob talks about the creation of a forward-deployed industrial base in the Philippines, where 4,000 acres will be developed into an “economic security zone.” He also compares and contrasts Pax Silica with China’s Belt and Road initiative, explains how the US plans to reindustrialize through automation and robotics, and explores how the Trump administration envisions making these policies durable across future presidencies. Plus, we hear why Jacob believes America to be a “global underdog.” Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jacobhelberg | @UnderSecE Chapters: 00:00 – Cold Open 00:41 – Jacob Helberg Introduction 01:02 – Pax Silica’s Mission 03:51 – Investing in AI Chip Supply Chains 05:43 – Comparing Pax Silica to China’s Belt and Road Initiative 12:38 – Pax Silica’s Value Proposition 14:38 – US vs. Partnered Manufacturing 19:10 – Rare Earth Mineral Pricing 22:16 – Role of Venture Capital in Pax Silica 24:50 – Near vs. Long-Term Priorities 27:09 – Making AI Policy Durable 28:09 – How Policies Impact Entrepreneurs 31:00 – Trump’s Entrepreneurial Administration 33:00 – Why America is a Global Underdog 38:00 – Conclusion

The world’s first AI-take-private just proved that AI can revolutionize the real economy. Long Lake Management co-founder and CEO Alexander Taubman joins Elad Gil to discuss his firm’s agreement to acquire the legacy platform American Express Global Business Travel (Amex GBT) in a deal valued at $6.3 billion. Alexander explains the mechanics of AI-driven roll-ups, and why Long Lake chooses to acquire and transform businesses rather than simply selling them software. He also talks about how Long Lake’s horizontal AI platform, Nexus, automates workflows across diverse verticals, and how automation through AI not only powers growth for their portfolio companies, but results in both satisfied customers and employees. Plus, they explore Alexander’s vision of Amex GBT as a multi-decade compounding machine.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alextaubman | @amexgbt  Chapters: 00:00 – Alexander Taubman Introduction 00:30 – Long Lake’s Nexus Platform 03:35 – Retention and Talent Flywheel 05:01 – Acquisition vs. Offering Software 06:57 – Building Long Lake’s Founding Team 10:37 – Taking American Express Global Business Travel Private 13:36 – Taking Berkshire Hathaway’s Approach to Management 16:37 – How AI Strategy Makes Long Lake Stand Out  19:32 – AI Makes Services Scale 22:00 – Conclusion

Baseten CEO and co-founder Tuhin Srivastava sits down with Sarah Guo and Elad Gil to discuss the rapid growth of AI inference demand, Baseten’s 30x growth, and why inference is becoming the strategic “last market.” Tuhin Srivastava argues the application layer will persist because companies with unique user signals can encode value into workflows and post-train specialized models, citing examples like Abridge and support workflows. The conversation covers GPU capacity constraints, Baseten’s multi-cloud fabric across 18 clouds and 90 clusters, long-term contracting dynamics, the importance of the software layer for stickiness, evolving workloads, multichip possibilities, and operational lessons at scale. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Tuhinone   Chapters: 00:31 Baseten growth 01:55 Why the app layer wins 05:57 Serving frontier customers 07:55 Open source model mix 09:21 Chinese models and geopolitics 13:07 Custom inference dominates 14:22 Post training acquisition 17:10 When to invest in custom models 18:35 Supply crunch and data centerse 22:25 Longer GPU Contracts 24:09 What Makes a Winner 26:07 Multi Chip Future 28:19 Runtime Roadmap 31:08 Scaling Edge Cases 33:48 Hiring and Leadership 36:44 Operations Pager Culture 38:19 Efficiency Drives Demand 40:41 Concierge Everything Future 42:34 Conclusion

More than fifty years ago, the modern idea of the standard enterprise software was birthed at SAP. Now, after managing companies through technological shifts from the mainframe to mobile, SAP is at the forefront of closing the AI adoption gap for their customers. SAP Chief Technology Officer Philipp Herzig joins Sarah Guo to talk about how SAP has remained a durable end-to-end “operating system” for its more than 400,000 customers from finance to supply chain. Philipp argues that the AI transition in businesses should focus on customer outcomes, UI changes, business processes, and the data layer. He also explains the challenges in enterprise AI adoption, including security, scaling, and data fragmentation, as well as the importance of evals and verifiability. They also discuss SAP’s suite of AI products, limitations of predictive tabular models, how SAP is shifting its pricing models in the AI era, and Philipp’s interest in quantum computing optimization. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @pheartig | @SAP Chapters: 00:00 – Cold Open 00:42 – Philipp Herzig Introduction 01:18 – What SAP Does 02:51 – Why SAP Endures 06:53 – CTO Priorities and AI Push 12:14 – Scaling AI in Enterprise 17:06 – Verifiability and Agent Mining 20:42 – Tool Calling vs. Computer Use 22:11 – Domains Where Agents Deliver Value 24:58 – Limitations of Predictive Tabular Models 29:07 – Barriers to Enterprise Adoption 31:54 – How AI Will ‘Uplevels’ Work 34:03 – How AI Changes SAP’s Pricing Model 36:41 – What Makes a Winner in the AI Era 38:53 – Day in the Life of a CTO 40:08 – Customer Challenges 42:36 – Business Problem of Quantum Computing 46:21 – Conclusion

Few teens are business owners, but by age 16, Bill McDermott had purchased and was running a local deli. Now he runs leading global technology powerhouse ServiceNow, a company that is defining how the world’s largest organizations transform for the digital age. Sarah Guo sits down with ServiceNow CEO Bill McDermott to discuss his journey from child entrepreneur to CEO, and how he navigates his role as a leader in the age of AI. Bill argues that human connection is still a vital part of being a successful leader, and as such, AI must be used to serve people rather than substitute for ambition. He breaks down the mechanics of hyper-growth, and the art of staying customer-centric at a global scale. They also discuss the future of enterprise software, how generative AI is fundamentally reshaping the labor market, and what founders need to know about building a resilient company culture that survives economic and technological shifts. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @BillRMcDermott | @ServiceNow Chapters: 00:00 – Cold Open 00:50 – Bill McDermott Introduction 01:14 – Lesson from Buying a Deli 07:35 – Leadership in the AI Era 09:41 – How Bill Got Hired at Xerox 15:47 – Can Agency Be Taught? 18:40 – Seeing Change as Opportunity 25:18 – ServiceNow as an AI Control Tower 30:30 – Which SaaS Gets Disrupted? 32:22 – Defining a Platform Business 36:25 – Does AI Decrease Implementation Time? 39:06 – Agents Will Reshape the Workforce 40:59 – Success Signals at ServiceNow 44:07 – Enterprise Attitudes About AI 48:41 – How AI Has Changed Customer Conversations 50:48 – Bill’s Curiosity Beyond ServiceNow 52:29 – Day in the Life of a CEO 57:27 – Conclusion

AI agents can already collaborate, but they lack a trustworthy medium in which to store value and execute contracts. Enter Circle’s Arc Blockchain, an economic “operating system” designed for a world where machines drive the real economy. Circle co-founder and CEO Jeremy Allaire joins Elad Gil to dive into the future of programmable money and the agentic economy. Jeremy explains why traditional banking fails to support the needs of AI agents, and how stablecoins like USDC facilitate an internet-native economy. They also discuss the tokenization of real-world assets, the move toward full-reserve banking, and Jeremy’s predictions for double-digit GDP growth as AI and blockchain reach their “broadband moment.”  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jerallaire | @circle Chapters: 00:00 – Cold Open 00:05 – Jeremy Allaire Introduction 00:21 – Origin Story of Circle 02:11 – Rethinking the Financial System 05:26 – The Role of Stablecoins 09:52 – Use Cases for USDC 11:30 – Programmable Money  12:25 – Blockchain as Operating System 14:37 – The Agentic Economy 17:45 – Arc Blockchain Use Cases 27:00 – Scaling Models and Privacy Tech 30:45 – Securitization of Other Assets Under the Blockchain 34:16 – Prediction Markets 35:09 – Incremental Revenue Through GPU Usage 37:19 – Jeremy’s 10 Year Future Vision 41:12 – AI and GDP 44:00 – Conclusion

What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with Liam Fedus, co-founder at Periodic Labs, which is pioneering an AI foundation lab for atoms. Liam discusses how he pivoted from dark matter physics research to the front lines of artificial intelligence, including stints at Google Brain and working on ChatGPT at OpenAI. He talks about how Periodic is connecting massive language models to the physical world to overcome data bottlenecks in material science. Liam also shares how they use language models as an orchestration layer operating alongside specialized neural nets to run closed-loop physical experiments. They also explore the future of AGI and ASI, as well as the role of robotics in lab automation. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LiamFedus | @periodiclabs Chapters: 00:00 – Cold Open 00:05 – Liam Fedus Introduction 00:39 – Liam’s Background at Google Brain, OpenAI 05:14 – From ChatGPT to Materials and Atoms 06:34 – Training Data in the Physical World 09:52 – Generalization Across Domains 11:31 – Models as an Orchestration Layer 12:48 – Commercialization and Business Model 16:10 – How Periodic’s Success May Shape the Future  17:45 – Multidisciplinary Scaling 19:41 – Capital and Compute 21:12 – Hiring at Periodic 21:44 – Thoughts on AGI and ASI 23:30 – Timeline for Machine-Directed Self-Improvement 25:39 – Automation and Data Generation 27:59 – Why Liam is Excited About the Future of Robotics 29:25 – Conclusion

What happens when AI agents can design experiments, collect data, and improve — without a human in the loop? Andrej Karpathy joins Sarah Guo on the state of models, the future of engineering and education, thinking about impact on jobs, and his project AutoResearch: where agents close the loop on a piece of AI research (experimentation, training, and optimization, autonomously). 00:00 Andrej Karpathy Introduction 02:55 What Capability Limits Remain? 06:15 What Mastery of Coding Agents Looks Like 11:16 Second Order Effects of Natural Language Coding 15:51 Why AutoResearch  22:45 Relevant Skills in the AI Era 28:25 Model Speciation 32:30 Building More Collaboration Surfaces for Humans and AI 37:28 Analysis of Jobs Market Data 48:25 Open vs. Closed Source Models 53:51 Autonomous Robotics 1:00:59 MicroGPT and Agentic Education 1:05:40 Conclusion

Notion isn’t designing AI agents that just use tools. Their agents can autonomously build their own integrations, as well as write the code needed to finish a task. Sarah Guo sits down with Notion Co-Founder Simon Last to explore Notion’s rapid evolution from a simple writing assistant to a sophisticated platform for custom AI agents. Simon discusses the technical hurdles of indexing disparate data from sources like Slack and Google Drive, as well as the internal shift toward using coding agents to build Notion itself. Plus, Simon elaborates on what he sees as a fundamental transition in productivity: moving from a tool where humans do the work, to one where humans manage a swarm of agents. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @simonlast | @NotionHQ Chapters: 00:00 – Cold Open 00:05 – Simon Last Introduction 00:26 – Genesis of Notion AI 04:10 – Challenge of Semantic Indexing and Retrieval 07:16 – The Six-Month Rewrite Cycle 08:12 – Notion’s Coding Agent Era 09:44 – Impact on Team Dynamics 12:49 – Launching Custom Agents 15:39 – Notion as the ‘Switzerland’ for Models 17:33 – Designing APIs for Agent Customers 20:09 – Simon’s Personal Agentic Workflows 24:48 – Notion: Tool for Work is Now A Tool for Agents 27:28 – How Building Has Changed for Simon 29:00 – Conclusion

By the end of 2026, AI capital expenditure is projected to hit nearly $700 billion. The question isn’t who has the best model, but who has the most creative financing to build out AI infrastructure and beyond. Sarah Guo is joined by Neil Tiwari, Managing Director at Magnetar Capital, a financial innovator helping the AI industry scale from billions to trillions of dollars in CapEx. Neil explains some of the debt structures used to finance massive GPU clusters, who is taking the risk, and how the industry is maturing. Sarah and Neil also discuss how power distribution, energy storage, and physical materials like steel are the bottlenecks of the AI industry. Plus, Neil gives his take on the future of inference-optimized clouds, and why the market shift away from software and into infrastructure might be an overreaction. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Cold Open 00:05 – Neil Tiwari Introduction 00:26 – Magnetar’s Story 01:28 – Why CoreWeave Helped Magnetar Win 06:15 – Scaling CapEx Efficiently 09:02 – Debunking GPU Collateral Risk 11:42 – How Deal Structures Evolve 13:01 – What Bottlenecks Buildout 15:28 – Circular Financing Critiques 17:35 – The Shift from Training to Inference Workloads 23:10 – AI Factories 24:12 – Constraints of the Current Power Grid 28:27 – Sovereign Compute Buildouts 29:54 – Physical AI Capital Needs 32:48 – The Capital Rotation Away from SaaS 36:04 – Conclusion

In this episode of No Priors, Sarah and Elad dive into the evolving landscape of software, exploring how AI is transforming the traditional SaaS model. They discuss whether SaaS as we know it is coming to an end, what new business and sales strategies are emerging, and how AI is reshaping the way software is built, sold, and scaled. The conversation also examines whether or not these shifts are a good thing for both big and small companies, and how coders and software experts are reacting to abrupt AI transitions. They also dig into how AI is reshaping sales, automating workflows, and enabling more predictive customer strategies. Beyond individual companies, they examine how tech giants are increasingly dominating the S&P 500, and what this concentration of power means for the future of startups, innovation, and the broader entrepreneurial ecosystem. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil |  Chapters: 00:00 – Cold Open 00:35 – The SaaS-polcalypse discussion  4:55 – AI Change Management in Large vs. Small Companies 05:43 – “Is Software Eating the World?”  08:38 – Addressing the Unsolved Problems  14:00 – The Noise of the Last Month vs. Excitement  21:32  – What Proportion of GDP is Tech?  23:20 – Market Cap Shifts 25:02 – As a Company, When Should You Sell?  29:05 – Multi-Product Bundle Defense  30:45 – Conclusion

Autonomous vehicle technology has moved past human-coded rules and into an era of neural networks and custom computer chips. And to solve the most difficult driving scenarios, electric vehicle company Rivian abandoned its original technology platform to build a vertically integrated data stack. Sarah Guo sits down with Rivian Founder and CEO RJ Scaringe to explore the seismic shift in the automotive industry toward AI-driven, software-defined vehicles . RJ discusses the move away from function or domain-based architecture for vehicle electronic systems to software-defined architecture, which allows for dynamic, monthly updates to features in Rivian’s vehicles. RJ also talks about the upcoming launch of Rivian’s R2 model, which aims to be a distinct, affordable, mass-market alternative to the Tesla Model Y. Plus, RJ shares his vision for a future where vehicles don’t just drive us, but inspire personal freedom and exploration. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @RJScaringe | @Rivian  Chapters: 00:00 – Cold Open00:35 – RJ Scaringe Introduction0:58 – Rivian’s Autonomy Evolution05:19 – Why Rivian’s Tech is Vertically Integrated10:06 – Levels of Autonomous Driving Technologies14:00 – Importance of a Software-Defined Architecture19:28 – Differentiating Autonomous Vehicle Models23:20 – R2: The First Mass Market Autonomous Vehicle25:02 – Do Americans Want EVs?29:05 – How Our Relationship to Vehicles is Evolving30:45 – Conclusion

From “virtual doppelgängers” to “real-time dreaming,” online gaming platform Roblox is using AI technology to build the “Holodeck” envisioned in science fiction decades ago. Sarah Guo and Elad Gil sit down with Roblox CEO Dave Baszucki at Roblox headquarters to explore the intersection of AI, physics simulation, and the future of human connection. Dave discusses the evolution of the 4D creation tool in Roblox, a high-fidelity simulation that enables thousands of people to interact in real-time with photo-realistic graphics and acoustic physics. Dave reveals how Roblox is leveraging 13 billion hours of monthly user data to train native AI models that go beyond simple LLMs, enabling NPCs that can navigate and play games with human-like intuition. He also talks about how immersive communication will change video conferencing, how Roblox searches for unlikely talent outside of traditional elite universities, and how he balances rapid weekly iterations with keeping a “long view” on Roblox’s vision.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @DavidBaszucki | @Roblox  Chapters: 00:00 – Cold Open 00:36 – Dave Baszucki Introduction 01:16 – Realizing Robolox’s 20-Year Vision 05:29 – Using 4D Immersive Simulations in Virtual Interactions 08:22 – Physics Engine vs. Photorealism  11:50 – Storing Roblox History as Vector Data 14:00 – Training NPCs - Moving Beyond LLMs 18:05 – The Future of the Game Designer 19:54 – Video Latent World Models 23:53 – Social Simulation - AI Companions and Virtual Relationships 27:26 – Why Asset Costs Haven’t Changed the Gaming Industry 29:52 – AI Coding in Roblox Studio 31:36 – The Roblox Creator Economy 33:57 – Long-Term Conviction vs. Weekly Iteration 37:50 – Dave’s Hiring Philosophy for Roblox 43:44 – Conclusion

What if we could pause biological time to wait for a cure for a disease? Thanks to innovations and research in reversible cryopreservation, this possibility is no longer just science fiction. Sarah Guo sits down with Laura Deming, CEO and co-founder of biotech startup Until, to dive deep into the growing field of reversible cryopreservation. Laura talks about how her time as a Thiel Fellow as well as her founding of the Longevity Fund fueled her obsession with solving the “social blindspot” of aging. Laura details how her new startup, Until, seeks to build tools that allow for “pressing pause” on biological time, starting with human organs with the hopes of scaling up to full body medical hibernation. Together, they also discuss why ice is the enemy of tissue, using engineering tools to help solve biological problems, and how this technology may revolutionize organ transplantation by removing time as a variable.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LauraDeming | @untillabs  Chapters: 00:00 – Cold Open 01:08 – Laura Deming Introduction 01:53 – Why Laura Focused on Cryo Preservation and Longevity 06:20 – Bringing on Co-Founder Hunter Davis 07:55 – Until’s Goal 10:10 – Other Use Cases for Cryo Technology 12:22 – Scientific Challenges in Cryo Tech 15:36 – Using Engineering Principles to Solve Biological Problems 20:18 – Scaling Up Cryo Preservation 21:48 – Leading and Recruiting at Until 25:02 – Why Hasn’t Cryo Tech Been Worked On More? 27:14 – Making Time Not a Variable in Organ Transplants  29:06 – Changing How the Molecular World is Depicted 30:47 – Conclusion

Why are there only a handful of companies in the world with over $10 billion in pure-play software revenue? CJ Desai believes the reason is that products are replaceable, but platforms are forever. For No Priors’ very first live from MongoDB.local SF, Sarah Guo is joined by CJ Desai, CEO and President of software developer MongoDB, to discuss the shifting landscape of enterprise software. CJ discusses whether AI will erode the value of software, and what truly constitutes a “moat” in the age of generative AI. CJ also talks about why AI adoption with Fortune 500-sized companies is still lagging, the importance of customer relationships, and why the “bear thesis” on SaaS may be overblown.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @cj_mongodb | @MongoDB Chapters: 00:00 – Cold Open 00:58 – CJ Desai Introduction 01:38 – The AI Stack and the Future of Software 04:18 – Why Platforms, Not Products, Are Sticky 09:59 – Vibe Coding and the Threat of On-Demand Apps 12:15 – Paths to Success for Software Vendor Incumbents 14:24 – How CJ Chose MongoDB 18:55 – Debunking the SaaS Bear Thesis 22:07 – Fortune 500 Perspectives on AI Value 24:24 – Can AI Native Startups Replace Systems of Record? 28:10 – The Importance of Customer Relationships 31:46 – Managing Through Massive Technology Transitions 36:37 – Conclusion

Today’s arms race looks a little different from those of the past. Under the Trump administration, the US Department of War (DoW) is deploying generative AI to millions of employees in order to maintain a strategic edge over our global adversaries. Sarah Guo and Elad Gil sit down with Emil Michael, the Under Secretary of War for Research and Engineering of the United States, to discuss the radical technological transformation of the US military. Emil outlines the architecture and launch of GenAI.mil, a DoW internal AI platform powered by Gemini and Grok that reached over one million unique users in its first 30 days. He also highlights critical technology priorities for national security, including hypersonics, direct energy, and autonomous drone swarms. Together, they also explore the urgent need to rebuild the American defense industrial base and end dependency on foreign supply chains for critical materials, as well as how Emil is recruiting the next generation of “fixer-builder” workers to serve their country in government.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @USWREMichael | @DoWCTO Chapters: 00:00 – Cold Open 00:00 – Emil Michael Introduction 00:58 – Emil’s Role at the Department of War 05:22 – Innovation Priorities for the DoW 08:27 – Shift Toward Autonomous Defense Technologies 10:41 – Identifying Common Needs Across the DoW 12:02 – Architecting GenAI.mil 13:48 – Applied AI Initiatives at the DoW 15:57 – The Future of Warfare 17:55 – Recruiting for DoW 19:33 – Arsenal of Freedom Tour 22:25 – Opportunities for Entrepreneurs at DoW 25:49 – Speeding Up and Scaling DoW Initiatives 28:37 – Innovation in Defense Tech 30:00 – Change Management in Government 32:09 – Rebuilding the Defense Industrial Base 37:27 – Initiatives and Opportunities at the Office of Strategic Capital 41:41 – Lessons from Emil’s Government Experience 44:30 – Conclusion

Even if ChatGPT never existed, the tech giant NVIDIA would still be winning. The end of Moore’s Law—says NVIDIA President, Founder, and CEO Jensen Huang—makes the shift to accelerated computing inevitable, regardless of any talk of an AI “bubble.” Sarah Guo and Elad Gil are joined by Jensen Huang for a wide-ranging discussion on the state of artificial intelligence as we begin 2026. Jensen reflects on the biggest surprises of 2025, including the rapid improvements in reasoning, as well as the profitability of inference tokens. He also talks about why AI will increase productivity without necessarily taking away jobs, and how physical AI and robotics can help to solve labor shortages. Finally, Jensen shares his 2026 outlook, including why he’s optimistic about US-China relations, why open source remains essential for keeping the US competitive, and which sectors are due for their “ChatGPT moment.”  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @nvidia Chapters: 00:00 – Jensen Huang Introduction 00:17 – Biggest AI Surprises of 2025 04:12 – AI and Jobs: New Infrastructure and Demand for Skilled Labor 09:03 – Task vs. Purpose Framework in Labor 12:31 – Solving Labor Shortages with Robotics 15:14 – The Layer Cake of AI Technology 18:39 – The Importance of Open Source 21:52 – The Myth of “God AI” and Monolithic Models 23:54 – Addressing the “Doomer” Narrative and Regulation 29:25 – The Plummeting Cost of Compute and Tokenomics 35:09 – The Return to Research 37:49 – Future of Coding and Software Engineering 43:20 – The Industries Due For Their “ChatGPT” Moments 46:00 – The Evolution of Self-Driving Cars and Robotics 54:06 – Energy Demand and Growth for AI 58:49 – 2026 Outlook: US-China Relations and Geopolitics 1:04:43 – Is There An AI Bubble? 1:16:20 – Conclusion

Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking. Plus, stick around to hear predictions on what’s next for AI in 2026 from some of tech’s biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis). Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Introduction 02:43 – AI Predictions for 2026 04:40 – Adoption of AI in Professional Fields 07:17 – Robotics and Self-Driving Cars 08:25 – Robotics: Incumbents vs. Startups 13:59 – Future of IPOs and M&A in AI 16:42 – Challenges in Consumer AI Innovation 21:08 – Funding of Neo Labs, RL Research 26:28 – Predictions for 2026 Beyond AI 26:44 – The Future of Defense and Technology 28:23 – Biohacking and Peptide Therapies 30:37 – 2026 Prediction from AI Industry Leaders 40:46 – Conclusion

Imagine learning chess from a grand master, or negotiating tactics from an expert FBI hostage negotiator. ElevenLabs’ voice AI technology is making that unlock possible. Sarah Guo sits down with Mati Staniszewski, co-founder of ElevenLabs, to explore how the three-year old company is transforming how humans interact with technology through voice. Mati talks about the technical challenges of building foundational audio models, the strategic thinking between conducting research and deploying products in tandem, and why voice is the ultimate interface for everything from computers to robots to immersive media. They also discuss how the coming revolution of AI personal tutors will shift agentic AI from reactive to proactive support, break down language barriers globally, and even provide the framework for agentic government services. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @elevenlabsio |@matiii Chapters: 00:00 – Mati Staniszewski Introduction 00:46 – 11 Labs: Growth and Scale 02:46 – Voice Technology and Applications 06:52 – Research and Product Development 12:36 – Voice Quality and Customer Preferences 17:54 – Agent Platform and Use Cases 23:21 – Choosing the Right Technology Partner 26:43 – The Role of Foundation Models 29:58 – Open Source Models and Future Trends 32:37 – Research and Development Focus 36:53 – Future of AI Companions and Education 41:37 – Conclusion

In just over three years, Harvey has not only scaled to nearly one thousand customers, including Walmart, PwC, and other giants of the Fortune 500, but fundamentally transformed how legal work is delivered. Sarah Guo and Elad Gil are joined by Harvey’s co-founder and president Gabe Pereyra to discuss why the future of legal AI isn’t only about individual productivity, but also about putting together complex client matters to make law firms more profitable. They also talk about how Harvey analyzes complex tasks like fund formation or M&A and deploys agents to handle research and drafting, the strategic reasoning behind enabling law firms rather than competing with them, and why AI won’t replace partners but will change law firm leverage models and training for associates. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @gabepereyra | @Harvey Chapters: 00:00 – Gabe Pereyra Introduction 00:09 – Introduction to Harvey 02:04 – Expanding Harvey’s Reach 03:22 – Understanding Legal Workflows 06:20 – Agentic AI Applications in Law 09:06 – The Future Evolution of Law Firms 13:36 – RL in Law 19:46 – Deploying Harvey and Customization 23:46 – Adoption and Customer Success 25:28– Why Harvey Isn’t Building a Law Firm 27:25 – Challenges and Opportunities in Legal Tech 29:26 – Building a Company During the Rise of Gen AI 37:24 – Hiring at Harvey 40:19 – Future Predictions 44:17 – Conclusion 

The robotics industry is on the cusp of its own “GPT” moment, catalyzed by transformative research advances. Enter Memo, the first general-intelligence personal robot, focused on taking on your chores to give back your time. Sarah Guo sits down with Tony Zhao and Cheng Chi, co-founders of Sunday Robotics, to discuss the state of AI robotics. Tony and Cheng speak to the challenges they faced while developing their technology, the innovative glove system employed to scale real-world data collection, and the impact of diffusion policy and imitation learning. Plus, they talk about their 2026 in-home beta program and why personal robots are only a handful of years away from mass deployment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @tonyzzhao | @chichengcc | @sundayrobotics Chapters: 00:00 – Tony Zhao and Cheng Chi Introduction 00:56 – State of AI Robotics 02:11 – Deploying a Robot Pre-AI 03:13 – Impact of Diffusion Policy  04:29 – Role of ACT and ALOHA 07:02 – Imitation Learning - Enter UMI 10:38 – Introducing Sunday 11:57 – Sunday’s Robot Design Philosophy 15:05 – Sunday’s Shipping Timeline 19:02 – Scale of Sunday’s Training Data 23:58 – Importance of Data Quality at Scale 24:56 – Technical Challenges 27:59 – When Will People Have Home Robots? 30:48 – Failures of Past Demos 32:34 – Sunday’s Demos 36:53 – What Sunday’s Hiring For 39:10 – Conclusion

Bringing new drugs to market is a costly, time-consuming endeavor. On top of that, most medicines fail at some point in the research and development phase. Sarah Guo is joined by Sajith Wickramasekara, co-founder and CEO of Benchling, a company that has not only become the central system of record for biotech R&D, but uses AI agents to assist scientists to help fix this broken system. Sajith details the roadblocks that impede drug development and approval, the “dot com” bust occurring in biotech, and how AI agents and simulation can help scientists experiment faster. Plus, they talk about China’s competitive rise in the pharma space, and the unique challenges of building an interdisciplinary culture that merges the worlds of science and software.  Rebuild biotech for the AI era - Sajith Wickramasekara Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @sajithw | @benchling Chapters: 00:00 – Sajith Wickramasekara Introduction 00:38 – Origin and Mission of Benchling 02:08 – The Drug Development Process 03:49 – Current State of the Biotech industry  08:46 – AI’s Role in Biotech 16:14 – Benchling AI and Its Impact 18:36 – The Future of AI in Biotech  26:28 – Debunking AI Drug Discovery Myths 28:50 – Data’s Role in Biotech 29:35 – The Importance of Tools in Pharma 31:28 – AI’s Impact on Scientific Research 34:55 – Building a Biotech Company 40:18 – Interdisciplinary Collaboration in Biotech  43:06 – Tech and Biotech: Learning from Each Other 48:16 – Conclusion

Snowflake is moving beyond the data warehouse. Its new Snowflake Intelligence is an agentic platform for every employee, not just data teams. Sarah Guo sits down with Snowflake CEO Sridhar Ramaswamy to discuss his first 18 months at the helm, as well as the massive pivot to make the data giant AI-first. Sridhar talks about Snowflake Intelligence, the company's new AI agent platform, and its implications for enterprise data management. They also explore how Sridhar navigates partnerships with major tech companies, how he fosters a culture of continuous improvement within the organization, and how he envisions Snowflake’s future as an integral data-driven enterprise solution.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Snowflake Chapters: 00:00 – Sridhar Ramaswamy Introduction 00:42 – Snowflake’s Market Adaptation 03:14 – Snowflake’s Evolution and AI Integration 05:44 – Introducing Snowflake Intelligence 09:01 – Snowflake Intelligence User Experience 11:55 – Drawing the Line Between Data, Agent System, and App 13:30 – Leadership and Organizational Changes 16:19 – How Being an Investor, Entrepreneur Informed Sridhar’s Leadership 18:50 – Importance of Product-Market Fit 22:46 – Snowflake’s Strategic Positioning 27:10 – Snowflake’s Partnership Strategy 30:20 – How Sridhar Sees the ROI of AI 35:09 – How AI Changes the Ad Model 38:15 – Why LLMs Still Need Search 42:11 – Conclusion

2025 has thus far been a year of great leaps and advances in AI technology. And Sarah and Elad have spoken with some of the most enterprising founders and scientific minds in the field of AI today. So we’re revisiting a few of our favorite conversations on No Priors so far in 2025 – Winston Weinberg (Harvey), Dr. Fei-Fei Li (World Labs), Brendan Foody (Mercor), Dan Hendrycks (Center for AI Safety), Noubar Afeyan (Flagship Pioneering), Brandon McKinzie and Eric Mitchell (OpenAI o3), Isa Fulford (OpenAI), Arvind Jain (Glen), and Dr. Shiv Rao (Abridge).  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Episode Introduction 0:21 – Winston Weinberg on Leaning into New Capabilities 02:01 – Dr. Fei-Fei Li on Spatial Intelligence 04:13 – Brendan Foody on AI Disruption in the Workforce 06:10 – Dan Hendrycks on the Geopolitics of Superintelligence 08:06 – Noubar Afeyan on Entrepreneurship   10:38 – Brandon McKinzie and Eric Mitchell on Reasoning Models 12:41 – Isa Fulford on Training Deep Research 13:49 – Arvind Jain on Innovating Enterprise Search 16:21 – Dr. Shiv Rao on AI’s Human Impact 18:58 – Conclusion

For decades, the developer terminal has remained largely unchanged. But for Warp CEO and co-founder Zach Lloyd, reinventing this core tool is the key to unlocking AI agents for coding, debugging, and automating the entire development process. Zach joins Elad Gil to discuss how seeing this opportunity for innovation led to Warp’s agentic terminal for developers. Zach talks about the phases of software development, from coding by hand to the current "develop by prompt" era, and the coming age of fully automated development. Plus, Zach and Elad explore the deep philosophical questions around intelligence versus consciousness in AI models, and what it would take to believe a computer program is truly aware. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @zachlloydtweets | @warpdotdev Chapters: 00:00 – Zach Lloyd Introduction 00:32 – AI, Intelligence, and Consciousness 06:55 – What Warp Does 07:38 – Benefits of the Terminal as a Launchpoint  08:27 – Features Driving Warp’s Adoption 09:12 – Zach’s View of the Coding Market 10:27 – Evolution of Coding Development 12:45 – Importance of Senior Engineer Expertise 14:11 – Future of Security and Other Dev Tools 22:22 – Why Zach Focused on the Terminal 23:52 – The Future of the Model Layer  25:36 – What Zach’s Excited About in the AI Dev World 27:18 – Conclusion

With demand from AI for energy already exploding, our electric grid is facing a crisis. Base Power CEO and co-founder Zach Dell is ready to re-architect its future from the ground up. Zach sits down with Elad Gil to talk about Base Power’s recent $1 billion fundraise from major investors. Zach discusses the role of energy across industries, as well as Base Power's mission to lower electricity costs through vertical integration. Zach and Elad also explore the future of energy, the role of batteries in transforming the grid, and the regulatory challenges facing the energy industry. Plus, Zach pitches why top talent should make their careers in energy generation.   Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ZachBDell | @basepowerco Chapters: 00:00 – Zach Dell Introduction 00:50 – Base Power’s Vision 02:15 – Base Power’s Products and Services  04:00 – What Drew Zach to Working on Power 05:12 – Base Power’s Founding Team 06:58 – Base Power’s Hiring Needs 08:02 – How Zach Hired an Awesome Founding Team 09:51 – How Do We Meet Energy Demands? 12:58 – How Viable is Nuclear Energy? 17:04 – Global Energy Cost Dynamics 17:41 – Future of AI Training Centers 18:32 – What Will Drive Energy Buildout 20:38 – Drivers of Energy Transmission Cost 22:30 – Regulation and the Energy Industry  23:52 – What Zach is Optimistic About in Energy 24:42 – Cultivating Base Power’s Culture 27:26 – Zach’s Philosophy on Capitalization 30:00 – How Base Power Uses Scale 31:57 – Conclusion

The AI industry is obsessed with making models smarter. But what if they’re building the wrong kind of intelligence? In launching his new venture, humans&, Eric Zelikman sees an opportunity to shift the focus from pure IQ to building models with EQ. Sarah Guo is joined by Eric Zelikman, formerly of Stanford and xAI, who shares his journey from AI researcher to founder. Eric talks about the challenges of building human-centric AI, integrating long-term memory in models, and the importance of creating AI systems that work collaboratively with humans to unlock their full potential. Plus, Eric shares his views on abunance and what he’s looking for in talent for humans&. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ericzelikman Chapters: 00:00 – Eric Zelikman Introduction 00:29 – Eric’s Early Interest in AI 01:29 – Challenges in AI and Automation 02:25 – Research Contributions 06:14 – Q-STaR and Scaling Up AI 08:14 – Current State of AI Models 15:23 – Human-Centric AI and Future Directions 22:08 – Eric’s New Venture: humans& 35:33 – Recruitment Goals for humans& 36:57 – Conclusion

Between the future of search, the biggest threats in cybersecurity, and the jobs and platforms of tomorrow, Nikesh Arora sees one common thread connecting and transforming them all—AI. Sarah Guo and Elad Gil sit down with Nikesh Arora, CEO of cybersecurity giant Palo Alto Networks and former Chief Business Officer of Google, to talk about a wide array of topics from agentic AI to leadership. Nikesh dives into the future of search, the disruptive potential of AI agents for existing business models, and how AI has both compressed the timeline for cyberattacks as well as fundamentally shifted defense strategies in cybersecurity. Plus, Nikesh shares his leadership philosophy, and why he’s so optimistic about AI. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @nikesharora | @PaloAltoNtwks Chapters: 00:00 – Nikesh Arora Introduction 00:39 – Nikesh on the Future of Search 04:46 – Shifting to an Agentic Model of Search 08:12 – AI-as-a-Service 16:55 – State of Enterprise Adoption 20:15 – Gen AI and Cybersecurity 27:35 – New Problems in Cybersecurity in the AI Age 29:53 – Deepfakes, Spearfishing, and Other Attacks 32:56 – Expanding Products at Palo Alto 35:49 – AI Agents and Human Replaceability 44:28 – Nikesh’s Thoughts on Growth at Scale 46:52 – Nikesh’s Leadership Tips 51:14 – Nikesh on Ambition 54:18 – Nikesh’s Thoughts on AI 58:21 – Conclusion

What if kids could master their academics in just two hours a day and spend the rest of their time developing real-world skills they’re passionate about? Joe Liemandt, founder of the software company Trilogy, is doing just that. Sarah Guo and Elad Gil are joined by Joe Liemandt, principal of Alpha School, to discuss his AI-driven vision of reinventing K-12 education. Joe talks about the strategies that Alpha School employs: reducing the traditional six-hour school day to two, replacing teachers with “Guides,” using financial incentives as motivation, and dedicating the remainder of the school day to project-based workshops that reflect the students’ passions. Together, they also examine Joe’s plan to scale Alpha School, the youth mental health crisis, and why edtech so far has failed. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AlphaSchoolATX Chapters: 00:00 – Joe Liemandt Introduction 00:27 – From Trilogy to Alpha School 02:45 – How Joe Changed His Mind About Alpha School 04:16 – Reenvisioning the School Day 09:06 – An Example Day at Alpha School 20:13 – Educating Based on Motivations 22:56 – Incentives-Based Learning 24:40 – Standards for Guides 26:39 – Extrinsic vs. Intrinsic Motivators 35:12 – Tackling Learning Differences  39:13 – Alpha School Pricing Structure 43:08 – Education Tech at Alpha School 44:54 – Rebuilding Education in the AI Age 48:43 – Reforming Education Policy 56:25 – Ed Tech as a Product 58:58 – Fixing Gaps in Education 59:45 – Why Education is Joe’s Mission 01:01:49 – Conclusion

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